{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "17884ae2-f111-4d3a-a1ba-7edf205079a8",
   "metadata": {},
   "source": [
    "## Multi-Factor Influence Mechanism of Charging Pile Utilization Rate and Regional Planning Implications: A Case Study of Shanghai"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c90b1c9d-dac1-4a2f-95ab-8d142486e3d3",
   "metadata": {},
   "source": [
    "## 1.Introduction\n",
    "Against the backdrop of rapid adoption of new energy vehicles, scientifically evaluating the adequacy of charging infrastructure has become critical for planning decisions. This study innovatively employs a cross-regional modeling approach, conducting regression analysis based on operational data from 1,000 charging piles to establish a utilization rate prediction model. After incorporating Shanghai's 2024 operational data, the model projects the city's average charging pile utilization rate for 2024 to be 14.8% ~ 18.8% (95% confidence interval), which fully aligns with the reasonable range (15% ~ 30%) defined by the China Charging Alliance (EVCIPA, 2023). These empirical findings demonstrate that Shanghai's charging infrastructure has reached a supply-demand equilibrium, where further large-scale expansion would yield diminishing marginal returns. Consequently, we recommend policymakers shift their focus from 'quantity expansion' to 'optimal allocation of existing resources'."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "081c154a-00c5-4f60-b5d1-183ad7532a49",
   "metadata": {},
   "source": [
    "## 2.Methodology\n",
    "### 2.1 Regression Model Specification "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40e7f497-d9e7-4251-a070-ea6ab0c230ea",
   "metadata": {},
   "source": [
    "This study employs a ​​full-variable inclusion and statistical screening​​ approach:\n",
    "\n",
    "1.Preliminary regression incorporates all available variables from the dataset.\n",
    "\n",
    "2.Retains only variables passing statistical significance tests (p < 0.05)\n",
    "The dataset contains [1000] observation records and [22] variables, which were imported for analysis after missing value treatment and outlier correction.\n",
    "\n",
    "First, we performed data loading and data cleaning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1cbb8773-6334-450d-877d-1623cfc3495e",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>charging_station_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>charging_duration</th>\n",
       "      <th>energy_consumed</th>\n",
       "      <th>no_of_evs_charging</th>\n",
       "      <th>traffic_flow</th>\n",
       "      <th>peak_traffic_hours</th>\n",
       "      <th>vehicle_type</th>\n",
       "      <th>avg_vehicle_speed</th>\n",
       "      <th>temperature</th>\n",
       "      <th>...</th>\n",
       "      <th>wind_speed</th>\n",
       "      <th>location_latitude</th>\n",
       "      <th>location_longitude</th>\n",
       "      <th>distance_to_nearest_road</th>\n",
       "      <th>charging_station_density</th>\n",
       "      <th>population_density</th>\n",
       "      <th>public_holiday_or_event</th>\n",
       "      <th>charging_price_per_kWh</th>\n",
       "      <th>government_incentives</th>\n",
       "      <th>charging_pile_usage_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2023-01-05 10:00:00</td>\n",
       "      <td>0.995879</td>\n",
       "      <td>17.443426</td>\n",
       "      <td>1</td>\n",
       "      <td>247</td>\n",
       "      <td>8</td>\n",
       "      <td>truck</td>\n",
       "      <td>33.173233</td>\n",
       "      <td>20.532106</td>\n",
       "      <td>...</td>\n",
       "      <td>10.871362</td>\n",
       "      <td>35.961448</td>\n",
       "      <td>121.051683</td>\n",
       "      <td>2.544341</td>\n",
       "      <td>1</td>\n",
       "      <td>3057</td>\n",
       "      <td>0</td>\n",
       "      <td>0.276276</td>\n",
       "      <td>0</td>\n",
       "      <td>1.470604</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>2023-01-13 22:00:00</td>\n",
       "      <td>0.617119</td>\n",
       "      <td>3.663894</td>\n",
       "      <td>2</td>\n",
       "      <td>497</td>\n",
       "      <td>8</td>\n",
       "      <td>sedan</td>\n",
       "      <td>54.092240</td>\n",
       "      <td>9.525288</td>\n",
       "      <td>...</td>\n",
       "      <td>11.427530</td>\n",
       "      <td>36.391309</td>\n",
       "      <td>120.357219</td>\n",
       "      <td>4.976256</td>\n",
       "      <td>1</td>\n",
       "      <td>3942</td>\n",
       "      <td>1</td>\n",
       "      <td>0.265587</td>\n",
       "      <td>1</td>\n",
       "      <td>1.430960</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>2023-01-17 15:00:00</td>\n",
       "      <td>0.795066</td>\n",
       "      <td>8.649020</td>\n",
       "      <td>4</td>\n",
       "      <td>301</td>\n",
       "      <td>18</td>\n",
       "      <td>sedan</td>\n",
       "      <td>46.175604</td>\n",
       "      <td>14.964203</td>\n",
       "      <td>...</td>\n",
       "      <td>13.463662</td>\n",
       "      <td>35.141676</td>\n",
       "      <td>120.387317</td>\n",
       "      <td>4.339104</td>\n",
       "      <td>5</td>\n",
       "      <td>3522</td>\n",
       "      <td>0</td>\n",
       "      <td>0.314434</td>\n",
       "      <td>1</td>\n",
       "      <td>1.987030</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   charging_station_id            timestamp  charging_duration  \\\n",
       "0                    1  2023-01-05 10:00:00           0.995879   \n",
       "1                    3  2023-01-13 22:00:00           0.617119   \n",
       "2                    4  2023-01-17 15:00:00           0.795066   \n",
       "\n",
       "   energy_consumed  no_of_evs_charging  traffic_flow  peak_traffic_hours  \\\n",
       "0        17.443426                   1           247                   8   \n",
       "1         3.663894                   2           497                   8   \n",
       "2         8.649020                   4           301                  18   \n",
       "\n",
       "  vehicle_type  avg_vehicle_speed  temperature  ...  wind_speed  \\\n",
       "0        truck          33.173233    20.532106  ...   10.871362   \n",
       "1        sedan          54.092240     9.525288  ...   11.427530   \n",
       "2        sedan          46.175604    14.964203  ...   13.463662   \n",
       "\n",
       "   location_latitude  location_longitude  distance_to_nearest_road  \\\n",
       "0          35.961448          121.051683                  2.544341   \n",
       "1          36.391309          120.357219                  4.976256   \n",
       "2          35.141676          120.387317                  4.339104   \n",
       "\n",
       "   charging_station_density  population_density  public_holiday_or_event  \\\n",
       "0                         1                3057                        0   \n",
       "1                         1                3942                        1   \n",
       "2                         5                3522                        0   \n",
       "\n",
       "   charging_price_per_kWh  government_incentives  charging_pile_usage_rate  \n",
       "0                0.276276                      0                  1.470604  \n",
       "1                0.265587                      1                  1.430960  \n",
       "2                0.314434                      1                  1.987030  \n",
       "\n",
       "[3 rows x 22 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "url=\"Demand Forecasting.zip\"\n",
    "df=pd.read_csv(url)\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e83bd405-cd1f-461a-bc99-9c87d5ecbcdd",
   "metadata": {},
   "source": [
    "Through preliminary statistical analysis, three categories of anomalies were identified in the raw data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "aa546b06-6a9e-4992-ac24-c0d93fda2002",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "34.9611229348208\n",
      "-9.99956605888362\n",
      "3.9191993705277417\n",
      "[ 1  5  4  3  6  7  2  9  8 10]\n"
     ]
    }
   ],
   "source": [
    "print(df['temperature'].max())\n",
    "print(df['temperature'].min())\n",
    "print(df['charging_pile_usage_rate'].max())\n",
    "print(df['charging_station_density'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a834dda-6531-47d4-b52d-dc3a200f11f9",
   "metadata": {},
   "source": [
    "Based on the aforementioned validation results, standardized processing was implemented:\n",
    "\n",
    "1.Temperature Data Correction\n",
    "Temperature values exceeding 15°C were identified as Fahrenheit measurements based on China's January climate patterns and geographic latitude analysis. Unit conversion was performed using the formula T(°C)=[T(°F)-32]/1.8 to ensure climatic consistency.\n",
    "\n",
    "2.Charging Pile Utilization Standardization\n",
    "Original utilization rates (typically >1) were verified to represent decadal percentage values (e.g., 1.5 indicating 15%). Uniform division by 10 converted these to standard 0-1 range values.\n",
    "\n",
    "3.Charging Station Density Calibration\n",
    "For the original 1-9 graded density data, linear transformation (uniform division by 5) was applied according to China's actual station distribution characteristics (referencing industry standards from the 2023 Annual Report on China's Charging Infrastructure Development), aligning density metrics with empirical observations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fb23df21-95a5-4a96-976a-4f2416951dad",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>charging_station_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>charging_duration</th>\n",
       "      <th>energy_consumed</th>\n",
       "      <th>no_of_evs_charging</th>\n",
       "      <th>traffic_flow</th>\n",
       "      <th>peak_traffic_hours</th>\n",
       "      <th>vehicle_type</th>\n",
       "      <th>avg_vehicle_speed</th>\n",
       "      <th>temperature</th>\n",
       "      <th>...</th>\n",
       "      <th>wind_speed</th>\n",
       "      <th>location_latitude</th>\n",
       "      <th>location_longitude</th>\n",
       "      <th>distance_to_nearest_road</th>\n",
       "      <th>charging_station_density</th>\n",
       "      <th>population_density</th>\n",
       "      <th>public_holiday_or_event</th>\n",
       "      <th>charging_price_per_kWh</th>\n",
       "      <th>government_incentives</th>\n",
       "      <th>charging_pile_usage_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>truck</td>\n",
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       "      <td>-6.371052</td>\n",
       "      <td>...</td>\n",
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       "      <td>2023-01-13 22:00:00</td>\n",
       "      <td>0.617119</td>\n",
       "      <td>3.663894</td>\n",
       "      <td>2</td>\n",
       "      <td>497</td>\n",
       "      <td>8</td>\n",
       "      <td>sedan</td>\n",
       "      <td>54.092240</td>\n",
       "      <td>9.525288</td>\n",
       "      <td>...</td>\n",
       "      <td>11.427530</td>\n",
       "      <td>36.391309</td>\n",
       "      <td>120.357219</td>\n",
       "      <td>4.976256</td>\n",
       "      <td>0.2</td>\n",
       "      <td>3942</td>\n",
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       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>2023-01-17 15:00:00</td>\n",
       "      <td>0.795066</td>\n",
       "      <td>8.649020</td>\n",
       "      <td>4</td>\n",
       "      <td>301</td>\n",
       "      <td>18</td>\n",
       "      <td>sedan</td>\n",
       "      <td>46.175604</td>\n",
       "      <td>14.964203</td>\n",
       "      <td>...</td>\n",
       "      <td>13.463662</td>\n",
       "      <td>35.141676</td>\n",
       "      <td>120.387317</td>\n",
       "      <td>4.339104</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3522</td>\n",
       "      <td>0</td>\n",
       "      <td>0.314434</td>\n",
       "      <td>1</td>\n",
       "      <td>0.198703</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   charging_station_id            timestamp  charging_duration  \\\n",
       "0                    1  2023-01-05 10:00:00           0.995879   \n",
       "1                    3  2023-01-13 22:00:00           0.617119   \n",
       "2                    4  2023-01-17 15:00:00           0.795066   \n",
       "\n",
       "   energy_consumed  no_of_evs_charging  traffic_flow  peak_traffic_hours  \\\n",
       "0        17.443426                   1           247                   8   \n",
       "1         3.663894                   2           497                   8   \n",
       "2         8.649020                   4           301                  18   \n",
       "\n",
       "  vehicle_type  avg_vehicle_speed  temperature  ...  wind_speed  \\\n",
       "0        truck          33.173233    -6.371052  ...   10.871362   \n",
       "1        sedan          54.092240     9.525288  ...   11.427530   \n",
       "2        sedan          46.175604    14.964203  ...   13.463662   \n",
       "\n",
       "   location_latitude  location_longitude  distance_to_nearest_road  \\\n",
       "0          35.961448          121.051683                  2.544341   \n",
       "1          36.391309          120.357219                  4.976256   \n",
       "2          35.141676          120.387317                  4.339104   \n",
       "\n",
       "   charging_station_density  population_density  public_holiday_or_event  \\\n",
       "0                       0.2                3057                        0   \n",
       "1                       0.2                3942                        1   \n",
       "2                       1.0                3522                        0   \n",
       "\n",
       "   charging_price_per_kWh  government_incentives  charging_pile_usage_rate  \n",
       "0                0.276276                      0                  0.147060  \n",
       "1                0.265587                      1                  0.143096  \n",
       "2                0.314434                      1                  0.198703  \n",
       "\n",
       "[3 rows x 22 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
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    }
   ],
   "source": [
    "df.loc[df['temperature'] > 15, 'temperature'] = (df['temperature'] - 32) * 5/9\n",
    "df['charging_station_density']=df['charging_station_density']/5\n",
    "df['charging_pile_usage_rate']=df['charging_pile_usage_rate']/10\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a96ed03-5aba-405b-a086-22a03009ae12",
   "metadata": {},
   "source": [
    "Based on the cleaned dataset, we constructed the regression model and performed variance inflation factor (VIF) testing：\n",
    "\n",
    "(The variable 'charging_duration' was excluded from the model due to perfect collinearity with 'charging_pile_usage_rate', as defined by the exact mathematical relationship: charging_pile_usage_rate = charging_duration/24 × 100%.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "99384d3d-89b0-492d-8258-27a632cc60bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                    Parameter Estimates                                     \n",
      "============================================================================================\n",
      "                          Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI\n",
      "--------------------------------------------------------------------------------------------\n",
      "energy_consumed              0.0041  8.886e-05     46.645     0.0000      0.0040      0.0043\n",
      "no_of_evs_charging           0.0213     0.0009     23.610     0.0000      0.0195      0.0231\n",
      "traffic_flow              3.747e-05  1.039e-05     3.6074     0.0003   1.708e-05   5.785e-05\n",
      "avg_vehicle_speed         8.741e-05   7.59e-05     1.1517     0.2497  -6.153e-05      0.0002\n",
      "temperature                 -0.0001     0.0002    -0.6411     0.5216     -0.0005      0.0003\n",
      "humidity                 -8.316e-05  7.431e-05    -1.1191     0.2634     -0.0002   6.267e-05\n",
      "precipitation                0.0020     0.0005     4.4680     0.0000      0.0011      0.0029\n",
      "wind_speed                   0.0008     0.0003     2.7883     0.0054      0.0002      0.0014\n",
      "location_latitude           -0.0024     0.0022    -1.0996     0.2718     -0.0066      0.0019\n",
      "location_longitude           0.0008     0.0021     0.3856     0.6999     -0.0034      0.0050\n",
      "distance_to_nearest_road    -0.0001     0.0010    -0.1349     0.8927     -0.0020      0.0018\n",
      "charging_station_density     0.0094     0.0023     4.1385     0.0000      0.0049      0.0138\n",
      "population_density        2.453e-07  1.106e-06     0.2217     0.8246  -1.926e-06   2.416e-06\n",
      "public_holiday_or_event      0.0085     0.0025     3.3429     0.0009      0.0035      0.0134\n",
      "charging_price_per_kWh      -0.0093     0.0112    -0.8314     0.4060     -0.0314      0.0127\n",
      "government_incentives        0.0013     0.0026     0.5168     0.6054     -0.0037      0.0063\n",
      "hour                        -0.0001     0.0002    -0.5534     0.5801     -0.0005      0.0003\n",
      "day_of_week                  0.0003     0.0010     0.3062     0.7595     -0.0016      0.0023\n",
      "month                        0.0506     0.2751     0.1840     0.8541     -0.4892      0.5904\n",
      "is_weekend                   0.0018     0.0046     0.3880     0.6981     -0.0072      0.0108\n",
      "============================================================================================\n",
      "                    Variable           VIF\n",
      "17                     month  49127.467165\n",
      "2               traffic_flow      1.026675\n",
      "8          location_latitude      1.026350\n",
      "7                 wind_speed      1.023338\n",
      "11  charging_station_density      1.022930\n",
      "13    charging_price_per_kWh      1.020966\n",
      "0            energy_consumed      1.017239\n",
      "3          avg_vehicle_speed      1.015242\n",
      "4                temperature      1.013184\n",
      "14     government_incentives      1.013171\n",
      "9         location_longitude      1.011329\n",
      "6              precipitation      1.010768\n",
      "15                      hour      1.010071\n",
      "1         no_of_evs_charging      1.008784\n",
      "16               day_of_week      1.007779\n",
      "10  distance_to_nearest_road      1.007689\n",
      "12        population_density      1.007635\n",
      "5                   humidity      1.006462\n"
     ]
    }
   ],
   "source": [
    "from linearmodels import PanelOLS\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "df['timestamp']=pd.to_datetime(df['timestamp'])\n",
    "df['charging_station_id']=df['charging_station_id'].astype(str) \n",
    "\n",
    "df['hour']=df['timestamp'].dt.hour\n",
    "df['day_of_week']=df['timestamp'].dt.dayofweek  \n",
    "df['is_weekend']=df['day_of_week'].isin([5, 6]).astype(int)\n",
    "df['month']=df['timestamp'].dt.month\n",
    "\n",
    "categorical_cols=['vehicle_type', 'peak_traffic_hours']\n",
    "df=pd.get_dummies(df, columns=categorical_cols, drop_first=True)\n",
    "\n",
    "df_panel=df.set_index(['charging_station_id', 'timestamp'])\n",
    "\n",
    "formula=\"\"\"\n",
    "charging_pile_usage_rate ~ \n",
    "energy_consumed +no_of_evs_charging +traffic_flow +avg_vehicle_speed +temperature +humidity +\n",
    "precipitation +wind_speed +location_latitude +location_longitude +distance_to_nearest_road +\n",
    "charging_station_density +population_density +public_holiday_or_event +charging_price_per_kWh +\n",
    "government_incentives +hour +day_of_week +month +is_weekend +EntityEffects\n",
    "\"\"\"\n",
    "\n",
    "mod=PanelOLS.from_formula(formula, data=df_panel)\n",
    "fe_res=mod.fit(cov_type='robust')\n",
    "\n",
    "\n",
    "print(fe_res.summary.tables[1])\n",
    "\n",
    "numeric_cols=[\n",
    "    'energy_consumed', 'no_of_evs_charging', \n",
    "    'traffic_flow', 'avg_vehicle_speed', 'temperature', 'humidity', \n",
    "    'precipitation', 'wind_speed', 'location_latitude', 'location_longitude',\n",
    "    'distance_to_nearest_road', 'charging_station_density', 'population_density',\n",
    "    'charging_price_per_kWh', 'government_incentives', 'hour', 'day_of_week', 'month'\n",
    "]\n",
    "\n",
    "X=df[numeric_cols]\n",
    "X=sm.add_constant(X) \n",
    "\n",
    "vif_data=pd.DataFrame()\n",
    "vif_data[\"Variable\"]=X.columns\n",
    "vif_data[\"VIF\"]=[variance_inflation_factor(X.values, i) for i in range(X.shape[1])]\n",
    "\n",
    "print(vif_data.sort_values(\"VIF\", ascending=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3c6fdc3-f854-47a0-84eb-19b61e7a8e1d",
   "metadata": {},
   "source": [
    "Based on the regression results, the 'month' variable was removed from the model due to its excessively high VIF value (VIF=49127.5), indicating severe multicollinearity that would harm the validity of the estimates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7bb10eeb-67d0-4192-a598-93dd897b41d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                    Parameter Estimates                                     \n",
      "============================================================================================\n",
      "                          Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI\n",
      "--------------------------------------------------------------------------------------------\n",
      "energy_consumed              0.0041  8.886e-05     46.645     0.0000      0.0040      0.0043\n",
      "no_of_evs_charging           0.0213     0.0009     23.610     0.0000      0.0195      0.0231\n",
      "traffic_flow              3.747e-05  1.039e-05     3.6074     0.0003   1.708e-05   5.785e-05\n",
      "avg_vehicle_speed         8.741e-05   7.59e-05     1.1517     0.2497  -6.153e-05      0.0002\n",
      "temperature                 -0.0001     0.0002    -0.6411     0.5216     -0.0005      0.0003\n",
      "humidity                 -8.316e-05  7.431e-05    -1.1191     0.2634     -0.0002   6.267e-05\n",
      "precipitation                0.0020     0.0005     4.4680     0.0000      0.0011      0.0029\n",
      "wind_speed                   0.0008     0.0003     2.7883     0.0054      0.0002      0.0014\n",
      "location_latitude           -0.0024     0.0022    -1.0996     0.2718     -0.0066      0.0019\n",
      "location_longitude           0.0008     0.0021     0.3856     0.6999     -0.0034      0.0050\n",
      "distance_to_nearest_road    -0.0001     0.0010    -0.1349     0.8927     -0.0020      0.0018\n",
      "charging_station_density     0.0094     0.0023     4.1385     0.0000      0.0049      0.0138\n",
      "population_density        2.453e-07  1.106e-06     0.2217     0.8246  -1.926e-06   2.416e-06\n",
      "public_holiday_or_event      0.0085     0.0025     3.3429     0.0009      0.0035      0.0134\n",
      "charging_price_per_kWh      -0.0093     0.0112    -0.8314     0.4060     -0.0314      0.0127\n",
      "government_incentives        0.0013     0.0026     0.5168     0.6054     -0.0037      0.0063\n",
      "hour                        -0.0001     0.0002    -0.5534     0.5801     -0.0005      0.0003\n",
      "day_of_week                  0.0003     0.0010     0.3062     0.7595     -0.0016      0.0023\n",
      "is_weekend                   0.0018     0.0046     0.3880     0.6981     -0.0072      0.0108\n",
      "============================================================================================\n",
      "Within R²: 0.6434\n",
      "Overall R²: 0.6435\n",
      "Between R²: 0.7422\n",
      "                    Variable       VIF\n",
      "2               traffic_flow  1.026675\n",
      "8          location_latitude  1.026350\n",
      "7                 wind_speed  1.023338\n",
      "11  charging_station_density  1.022930\n",
      "13    charging_price_per_kWh  1.020966\n",
      "0            energy_consumed  1.017239\n",
      "3          avg_vehicle_speed  1.015242\n",
      "4                temperature  1.013184\n",
      "14     government_incentives  1.013171\n",
      "9         location_longitude  1.011329\n",
      "6              precipitation  1.010768\n",
      "15                      hour  1.010071\n",
      "1         no_of_evs_charging  1.008784\n",
      "16               day_of_week  1.007779\n",
      "10  distance_to_nearest_road  1.007689\n",
      "12        population_density  1.007635\n",
      "5                   humidity  1.006462\n"
     ]
    }
   ],
   "source": [
    "formula_adjusted = formula.replace(\"month +\", \"\")\n",
    "\n",
    "mod_adjusted = PanelOLS.from_formula(formula_adjusted, data=df_panel)\n",
    "fe_res_adjusted = mod_adjusted.fit(cov_type='robust')\n",
    "\n",
    "print(fe_res_adjusted.summary.tables[1])\n",
    "print(f\"Within R²: {fe_res.rsquared:.4f}\") \n",
    "print(f\"Overall R²: {fe_res.rsquared_overall:.4f}\") \n",
    "print(f\"Between R²: {fe_res.rsquared_between:.4f}\")  \n",
    "\n",
    "numeric_cols_adjusted = [col for col in numeric_cols if col not in ['charging_duration', 'month']]\n",
    "\n",
    "X_adjusted = sm.add_constant(df[numeric_cols_adjusted])\n",
    "vif_data_adjusted = pd.DataFrame({\n",
    "    \"Variable\": X_adjusted.columns[1:], \n",
    "    \"VIF\": [variance_inflation_factor(X_adjusted.values, i) \n",
    "           for i in range(1, X_adjusted.shape[1])]\n",
    "})\n",
    "\n",
    "print(vif_data_adjusted.sort_values(\"VIF\", ascending=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c304cbf-8698-4312-aa78-1d219e7fee76",
   "metadata": {},
   "source": [
    "After variable screening with significance retention (retaining variables with p-value < 0.05), the final regression equation is as follows:\n",
    "\n",
    "charging_pile_usage_rate =0.0041×energy_consumed+0.0213×no_of_evs_charging+0.00003747×traffic_flow+0.0008×wind_speed+ 0.0020×precipitation+0.0094×charging_station_density+0.0085×public_holiday_or_event"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f49b9690-f903-480a-b9fa-998b79b978b2",
   "metadata": {},
   "source": [
    "To visually assess the goodness-of-fit of the regression equation, scatter plots of observed vs. predicted values and residual distribution plots were generated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8776b9df-0791-42aa-b22d-86758a1fc828",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "df['predicted'] = (0.0041 * df['energy_consumed'] + \n",
    "                   0.0213 * df['no_of_evs_charging'] +\n",
    "                   0.00003747 * df['traffic_flow'] + \n",
    "                   0.0008 * df['wind_speed'] +\n",
    "                   0.002 * df['precipitation'] + \n",
    "                   0.0094 * df['charging_station_density'] + \n",
    "                   0.0085 * df['public_holiday_or_event'])\n",
    "\n",
    "df['residuals'] = df['charging_pile_usage_rate'] - df['predicted']\n",
    "\n",
    "\n",
    "plt.figure(figsize=(15, 5))\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.scatter(df['charging_pile_usage_rate'], df['predicted'], alpha=0.6)\n",
    "plt.plot([df['charging_pile_usage_rate'].min(), df['charging_pile_usage_rate'].max()],\n",
    "         [df['charging_pile_usage_rate'].min(), df['charging_pile_usage_rate'].max()], \n",
    "         'r--')\n",
    "plt.title('Observed vs Predicted')\n",
    "plt.xlabel('observed')\n",
    "plt.ylabel('predicted')\n",
    "\n",
    "\n",
    "plt.subplot(1, 3, 3)\n",
    "plt.hist(df['residuals'], bins=30, density=True, alpha=0.6)\n",
    "plt.title('Residual distribution')\n",
    "plt.xlabel('residual')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7aeba92a-a9cf-49ed-883b-53fa4bddfdbf",
   "metadata": {},
   "source": [
    "Based on the above graphical analysis and R² values, the regression equation demonstrates adequate goodness-of-fit and can be reasonably applied for approximate predictions of charging_pile_usage_rate."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3ddcc10-d343-463d-bb96-51a8234c00d0",
   "metadata": {},
   "source": [
    "### 2.2 Model Application:Demand Estimation in Shanghai"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f5565ce-1add-4219-af95-c8f49190814e",
   "metadata": {},
   "source": [
    "Building on the panel regression model developed previously, this section conducts predictive analysis on charging pile usage_rate in Shanghai."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d91d0501-2816-4858-8426-72e98dd21c1a",
   "metadata": {},
   "source": [
    "Due to data collection limitations, this study employs per-charge-event mean approximation for variables including energy_consumed (energy consumption per charging session) and no_of_evs_charging (number of vehicles per charging session). Holiday data adjustments apply the empirically validated +40% (95% CI: 37.2%-42.8%) charging load premium coefficient established by Schauer et al. (2021), which was derived from transaction-level data analysis of 3,562 charging piles.\n",
    "\n",
    "Next, we load the essential datasets and perform necessary data processing and cleaning procedures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c3d4dc3d-5b50-4cba-af6c-f86fd4313bd5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>month</th>\n",
       "      <th>Charging Vehicles (10k units)</th>\n",
       "      <th>Charging Sessions (1k times)</th>\n",
       "      <th>Charging Volume (10k kWh)</th>\n",
       "      <th>Fast Charging Ratio</th>\n",
       "      <th>Fast Charging Sessions (1k times)</th>\n",
       "      <th>Fast Charging Volume (10k kWh)</th>\n",
       "      <th>Ultra-Fast Charging Sessions</th>\n",
       "      <th>BEV Energy Proportion</th>\n",
       "      <th>BEV Charging Vehicle Ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2024-01-01</td>\n",
       "      <td>93</td>\n",
       "      <td>1251</td>\n",
       "      <td>27616</td>\n",
       "      <td>0.467</td>\n",
       "      <td>449</td>\n",
       "      <td>12898</td>\n",
       "      <td>2936</td>\n",
       "      <td>0.850</td>\n",
       "      <td>0.691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2024-02-01</td>\n",
       "      <td>92</td>\n",
       "      <td>1001</td>\n",
       "      <td>23009</td>\n",
       "      <td>0.453</td>\n",
       "      <td>355</td>\n",
       "      <td>10430</td>\n",
       "      <td>2492</td>\n",
       "      <td>0.859</td>\n",
       "      <td>0.694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2024-03-01</td>\n",
       "      <td>95</td>\n",
       "      <td>1218</td>\n",
       "      <td>26304</td>\n",
       "      <td>0.461</td>\n",
       "      <td>436</td>\n",
       "      <td>12120</td>\n",
       "      <td>4367</td>\n",
       "      <td>0.843</td>\n",
       "      <td>0.698</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       month  Charging Vehicles (10k units)  Charging Sessions (1k times)  \\\n",
       "0 2024-01-01                             93                          1251   \n",
       "1 2024-02-01                             92                          1001   \n",
       "2 2024-03-01                             95                          1218   \n",
       "\n",
       "   Charging Volume (10k kWh)  Fast Charging Ratio  \\\n",
       "0                      27616                0.467   \n",
       "1                      23009                0.453   \n",
       "2                      26304                0.461   \n",
       "\n",
       "   Fast Charging Sessions (1k times)  Fast Charging Volume (10k kWh)  \\\n",
       "0                                449                           12898   \n",
       "1                                355                           10430   \n",
       "2                                436                           12120   \n",
       "\n",
       "   Ultra-Fast Charging Sessions  BEV Energy Proportion  \\\n",
       "0                          2936                  0.850   \n",
       "1                          2492                  0.859   \n",
       "2                          4367                  0.843   \n",
       "\n",
       "   BEV Charging Vehicle Ratio  \n",
       "0                       0.691  \n",
       "1                       0.694  \n",
       "2                       0.698  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url2=\"Shanghai_EV_Charging_Trend_Dataset.xlsx\"\n",
    "df2=pd.read_excel(url2, dtype={\"month\": str}) \n",
    "df2['month'] = pd.to_datetime(df2['month'], format='%Y.%m')\n",
    "df2=df2[df2['month'].dt.year == 2024]\n",
    "df2=df2.sort_values(by='month').reset_index(drop=True)\n",
    "df2.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bd1c9bcf-506a-48a3-8674-58b22ffeff2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>month</th>\n",
       "      <th>Charging Stations (k units)</th>\n",
       "      <th>Charging Piles (10k units)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2024-01-01</td>\n",
       "      <td>9.5</td>\n",
       "      <td>17.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2024-02-01</td>\n",
       "      <td>9.6</td>\n",
       "      <td>17.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2024-03-01</td>\n",
       "      <td>9.8</td>\n",
       "      <td>18.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       month  Charging Stations (k units)  Charging Piles (10k units)\n",
       "0 2024-01-01                          9.5                        17.7\n",
       "1 2024-02-01                          9.6                        17.9\n",
       "2 2024-03-01                          9.8                        18.3"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url3=\"Number of charging stations&piles.xlsx\"\n",
    "df3=pd.read_excel(url3,dtype={\"month\": str})\n",
    "df3['month'] = pd.to_datetime(df3['month'], format='%Y.%m')\n",
    "df3.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "91fa0215-3be4-4af3-9aaf-c9832d315177",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>month</th>\n",
       "      <th>Charging Vehicles (10k units)</th>\n",
       "      <th>Charging Sessions (1k times)</th>\n",
       "      <th>Charging Volume (10k kWh)</th>\n",
       "      <th>Fast Charging Ratio</th>\n",
       "      <th>Fast Charging Sessions (1k times)</th>\n",
       "      <th>Fast Charging Volume (10k kWh)</th>\n",
       "      <th>Ultra-Fast Charging Sessions</th>\n",
       "      <th>BEV Energy Proportion</th>\n",
       "      <th>BEV Charging Vehicle Ratio</th>\n",
       "      <th>energy_consumed</th>\n",
       "      <th>no_of_evs_charging</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2024-01-01</td>\n",
       "      <td>93</td>\n",
       "      <td>1251</td>\n",
       "      <td>27616</td>\n",
       "      <td>0.467</td>\n",
       "      <td>449</td>\n",
       "      <td>12898</td>\n",
       "      <td>2936</td>\n",
       "      <td>0.850</td>\n",
       "      <td>0.691</td>\n",
       "      <td>12.471830</td>\n",
       "      <td>5.254237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2024-02-01</td>\n",
       "      <td>92</td>\n",
       "      <td>1001</td>\n",
       "      <td>23009</td>\n",
       "      <td>0.453</td>\n",
       "      <td>355</td>\n",
       "      <td>10430</td>\n",
       "      <td>2492</td>\n",
       "      <td>0.859</td>\n",
       "      <td>0.694</td>\n",
       "      <td>12.841349</td>\n",
       "      <td>5.139665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2024-03-01</td>\n",
       "      <td>95</td>\n",
       "      <td>1218</td>\n",
       "      <td>26304</td>\n",
       "      <td>0.461</td>\n",
       "      <td>436</td>\n",
       "      <td>12120</td>\n",
       "      <td>4367</td>\n",
       "      <td>0.843</td>\n",
       "      <td>0.698</td>\n",
       "      <td>11.801125</td>\n",
       "      <td>5.191257</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       month  Charging Vehicles (10k units)  Charging Sessions (1k times)  \\\n",
       "0 2024-01-01                             93                          1251   \n",
       "1 2024-02-01                             92                          1001   \n",
       "2 2024-03-01                             95                          1218   \n",
       "\n",
       "   Charging Volume (10k kWh)  Fast Charging Ratio  \\\n",
       "0                      27616                0.467   \n",
       "1                      23009                0.453   \n",
       "2                      26304                0.461   \n",
       "\n",
       "   Fast Charging Sessions (1k times)  Fast Charging Volume (10k kWh)  \\\n",
       "0                                449                           12898   \n",
       "1                                355                           10430   \n",
       "2                                436                           12120   \n",
       "\n",
       "   Ultra-Fast Charging Sessions  BEV Energy Proportion  \\\n",
       "0                          2936                  0.850   \n",
       "1                          2492                  0.859   \n",
       "2                          4367                  0.843   \n",
       "\n",
       "   BEV Charging Vehicle Ratio  energy_consumed  no_of_evs_charging  \n",
       "0                       0.691        12.471830            5.254237  \n",
       "1                       0.694        12.841349            5.139665  \n",
       "2                       0.698        11.801125            5.191257  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['energy_consumed']=df2['Charging Volume (10k kWh)']/df3['Charging Piles (10k units)']\n",
    "df2['energy_consumed']=10*(df2['energy_consumed']/df2['Charging Sessions (1k times)'])\n",
    "df2['no_of_evs_charging']=df2['Charging Vehicles (10k units)']/df3['Charging Piles (10k units)']\n",
    "df2.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6b8b29e1-23cb-45a6-a40c-46ba96b307da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>month</th>\n",
       "      <th>Charging Stations (k units)</th>\n",
       "      <th>Charging Piles (10k units)</th>\n",
       "      <th>charging_density</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2024-01-01</td>\n",
       "      <td>9.5</td>\n",
       "      <td>17.7</td>\n",
       "      <td>1.527577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2024-02-01</td>\n",
       "      <td>9.6</td>\n",
       "      <td>17.9</td>\n",
       "      <td>1.543657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2024-03-01</td>\n",
       "      <td>9.8</td>\n",
       "      <td>18.3</td>\n",
       "      <td>1.575816</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       month  Charging Stations (k units)  Charging Piles (10k units)  \\\n",
       "0 2024-01-01                          9.5                        17.7   \n",
       "1 2024-02-01                          9.6                        17.9   \n",
       "2 2024-03-01                          9.8                        18.3   \n",
       "\n",
       "   charging_density  \n",
       "0          1.527577  \n",
       "1          1.543657  \n",
       "2          1.575816  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3['charging_density']=1000*df3['Charging Stations (k units)']/6219\n",
    "df3.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ecc4fe45-778c-4ca8-8aae-643f94a38cf5",
   "metadata": {},
   "source": [
    "After computing monthly per-charge event means, we temporally disaggregate the data to daily resolution with holiday effects incorporation. The monthly charging station density values are uniformly applied to all days within the respective month."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "af08283f-0c5c-4bb0-9c34-844086893d38",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>energy_consumed</th>\n",
       "      <th>no_of_evs_charging</th>\n",
       "      <th>public_holiday_or_event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2024-01-01</td>\n",
       "      <td>17.460563</td>\n",
       "      <td>7.355932</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2024-01-02</td>\n",
       "      <td>12.471830</td>\n",
       "      <td>5.254237</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2024-01-03</td>\n",
       "      <td>12.471830</td>\n",
       "      <td>5.254237</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date  energy_consumed  no_of_evs_charging  public_holiday_or_event\n",
       "0 2024-01-01        17.460563            7.355932                        1\n",
       "1 2024-01-02        12.471830            5.254237                        0\n",
       "2 2024-01-03        12.471830            5.254237                        0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "full_dates = pd.date_range('2024-01-01', '2024-12-31', name='date')\n",
    "df2 = (\n",
    "    df2.set_index('month')\n",
    "    .reindex(full_dates, method='ffill')\n",
    "    .reset_index()\n",
    "    .rename(columns={'index': 'date'})\n",
    ")\n",
    "\n",
    "holidays = [\n",
    "    '2024-01-01', \n",
    "    '2024-02-10', '2024-02-11', '2024-02-12', '2024-02-13', '2024-02-14', \n",
    "    '2024-02-15', '2024-02-16', '2024-02-17', \n",
    "    '2024-04-04', '2024-04-05', '2024-04-06', \n",
    "    '2024-05-01', '2024-05-02', '2024-05-03', '2024-05-04', '2024-05-05',\n",
    "    '2024-06-08', '2024-06-09', '2024-06-10',\n",
    "    '2024-09-15', '2024-09-16', '2024-09-17', \n",
    "    '2024-10-01', '2024-10-02', '2024-10-03', '2024-10-04',\n",
    "    '2024-10-05', '2024-10-06', '2024-10-07' \n",
    "]\n",
    "\n",
    "df2['is_holiday'] = df2['date'].astype(str).isin(holidays).astype(int)\n",
    "df2['is_weekend'] = (df2['date'].dt.dayofweek >= 5).astype(int)\n",
    "df2['public_holiday_or_event'] = df2['is_holiday'] | df2['is_weekend']\n",
    "\n",
    "df2.loc[df2['public_holiday_or_event'] == 1, 'energy_consumed'] = df2['energy_consumed'] * 1.4\n",
    "df2.loc[df2['is_weekend'] & (df2['public_holiday_or_event'] == 0), 'energy_consumed'] = df2['energy_consumed'] * 1.4\n",
    "df2.loc[df2['public_holiday_or_event'] == 1, 'no_of_evs_charging'] = df2['no_of_evs_charging'] * 1.4\n",
    "df2.loc[df2['is_weekend'] & (df2['public_holiday_or_event'] == 0), 'no_of_evs_charging'] = df2['no_of_evs_charging'] * 1.4\n",
    "\n",
    "df2=df2[['date', 'energy_consumed', 'no_of_evs_charging', 'public_holiday_or_event']]\n",
    "\n",
    "df2.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "db1f1593-e9f6-45ac-98c2-6ee0644f902e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>Charging Stations (k units)</th>\n",
       "      <th>Charging Piles (10k units)</th>\n",
       "      <th>charging_density</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2024-01-01</td>\n",
       "      <td>9.5</td>\n",
       "      <td>17.7</td>\n",
       "      <td>1.527577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2024-01-02</td>\n",
       "      <td>9.5</td>\n",
       "      <td>17.7</td>\n",
       "      <td>1.527577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2024-01-03</td>\n",
       "      <td>9.5</td>\n",
       "      <td>17.7</td>\n",
       "      <td>1.527577</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date  Charging Stations (k units)  Charging Piles (10k units)  \\\n",
       "0 2024-01-01                          9.5                        17.7   \n",
       "1 2024-01-02                          9.5                        17.7   \n",
       "2 2024-01-03                          9.5                        17.7   \n",
       "\n",
       "   charging_density  \n",
       "0          1.527577  \n",
       "1          1.527577  \n",
       "2          1.527577  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3['month'] = pd.to_datetime(df3['month'])\n",
    "df3=(\n",
    "    df3.set_index('month')                \n",
    "    .reindex(full_dates, method='ffill')   \n",
    "    .reset_index()                         \n",
    "    .rename(columns={'index': 'date'})   \n",
    ")\n",
    "df3.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6874c116-6ffc-46f2-abdb-bb80f3cfd4f3",
   "metadata": {},
   "source": [
    "For precipitation and wind speed data, this study obtained Shanghai's 2024 daily meteorological records via Open-Meteo API (geo-coordinates: 31.23°N, 121.47°E), enforcing unit consistency (mm/day for precipitation, km/h for wind speed) with df and removing invalid API returns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "71ace6ed-c751-41af-a6f3-b73544a5e1b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>wind_speed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2024-01-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2024-01-02</td>\n",
       "      <td>0.2</td>\n",
       "      <td>15.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2024-01-03</td>\n",
       "      <td>2.2</td>\n",
       "      <td>21.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2024-01-04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2024-01-05</td>\n",
       "      <td>0.2</td>\n",
       "      <td>13.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  precipitation  wind_speed\n",
       "0  2024-01-01            0.0         9.5\n",
       "1  2024-01-02            0.2        15.1\n",
       "2  2024-01-03            2.2        21.7\n",
       "3  2024-01-04            0.0        13.9\n",
       "4  2024-01-05            0.2        13.6"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import requests\n",
    "def get_weather():\n",
    "    url = \"https://archive-api.open-meteo.com/v1/archive\"\n",
    "    params = {\n",
    "        \"latitude\": 31.23,\n",
    "        \"longitude\": 121.47,\n",
    "        \"start_date\": \"2024-01-01\",\n",
    "        \"end_date\": \"2024-12-31\",\n",
    "        \"daily\": [\"precipitation_sum\", \"wind_speed_10m_max\"],\n",
    "        \"timezone\": \"Asia/Shanghai\",\n",
    "        \"precipitation_unit\": \"mm\",\n",
    "        \"wind_speed_unit\": \"kmh\" \n",
    "    }\n",
    "    \n",
    "    response = requests.get(url, params=params)\n",
    "    data = response.json()\n",
    "    \n",
    "    df4 = pd.DataFrame({\n",
    "        \"date\": data[\"daily\"][\"time\"],\n",
    "        \"precipitation\": data[\"daily\"][\"precipitation_sum\"],\n",
    "        \"wind_speed\": data[\"daily\"][\"wind_speed_10m_max\"]\n",
    "    })\n",
    "    return df4\n",
    "df4=get_weather()\n",
    "df4.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95b96d4c-050c-443d-820b-cce876d9b5f0",
   "metadata": {},
   "source": [
    "Subsequently, we merged DataFrames df2, df3, and df4 by date to consolidate all regression-required variables into a single table. This integration facilitates both final model implementation and intuitive data visualization."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "358e7641-ea2f-4a85-9e0a-80e65fc63ca4",
   "metadata": {},
   "source": [
    "Meanwhile,given significant data acquisition constraints (only October's complete traffic flow data was available) and the relatively low regression contribution of the traffic flow parameter (β = 3.747e-05, p = 0.0003), this study adopts the following alternative approach:\n",
    "\n",
    "The October daily average traffic flow (x̄ = 4.359 million vehicles/day) is multiplied by the proportion of charging duration per event in the dataset (483.75 × 1.5 ÷ 16 = 40.9) to derive the per-charging-event mean traffic flow. This value serves as the standardized input parameter for full-period estimation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "36acfbdd-54ac-4fc8-9f54-1082f71a8365",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>energy_consumed</th>\n",
       "      <th>no_of_evs_charging</th>\n",
       "      <th>public_holiday_or_event</th>\n",
       "      <th>charging_density</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>wind_speed</th>\n",
       "      <th>traffic_flow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2024-01-01</td>\n",
       "      <td>17.460563</td>\n",
       "      <td>7.355932</td>\n",
       "      <td>1</td>\n",
       "      <td>1.527577</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>40.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2024-01-02</td>\n",
       "      <td>12.471830</td>\n",
       "      <td>5.254237</td>\n",
       "      <td>0</td>\n",
       "      <td>1.527577</td>\n",
       "      <td>0.2</td>\n",
       "      <td>15.1</td>\n",
       "      <td>40.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2024-01-03</td>\n",
       "      <td>12.471830</td>\n",
       "      <td>5.254237</td>\n",
       "      <td>0</td>\n",
       "      <td>1.527577</td>\n",
       "      <td>2.2</td>\n",
       "      <td>21.7</td>\n",
       "      <td>40.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date  energy_consumed  no_of_evs_charging  public_holiday_or_event  \\\n",
       "0 2024-01-01        17.460563            7.355932                        1   \n",
       "1 2024-01-02        12.471830            5.254237                        0   \n",
       "2 2024-01-03        12.471830            5.254237                        0   \n",
       "\n",
       "   charging_density  precipitation  wind_speed  traffic_flow  \n",
       "0          1.527577            0.0         9.5          40.9  \n",
       "1          1.527577            0.2        15.1          40.9  \n",
       "2          1.527577            2.2        21.7          40.9  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5=pd.concat([df2, df3['charging_density'], df4[['precipitation','wind_speed']]], axis=1)\n",
    "df5['date'] = pd.to_datetime(df5['date'], unit='D')\n",
    "df5['traffic_flow']=40.9\n",
    "df5.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a50d9f21-8a7d-4df0-a8fc-ba552ec0d3fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>energy_consumed</th>\n",
       "      <th>no_of_evs_charging</th>\n",
       "      <th>public_holiday_or_event</th>\n",
       "      <th>charging_density</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>wind_speed</th>\n",
       "      <th>traffic_flow</th>\n",
       "      <th>usage_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2024-01-01</td>\n",
       "      <td>17.460563</td>\n",
       "      <td>7.355932</td>\n",
       "      <td>1</td>\n",
       "      <td>1.527577</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>40.9</td>\n",
       "      <td>0.22827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2024-01-02</td>\n",
       "      <td>12.471830</td>\n",
       "      <td>5.254237</td>\n",
       "      <td>0</td>\n",
       "      <td>1.527577</td>\n",
       "      <td>0.2</td>\n",
       "      <td>15.1</td>\n",
       "      <td>40.9</td>\n",
       "      <td>0.16305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2024-01-03</td>\n",
       "      <td>12.471830</td>\n",
       "      <td>5.254237</td>\n",
       "      <td>0</td>\n",
       "      <td>1.527577</td>\n",
       "      <td>2.2</td>\n",
       "      <td>21.7</td>\n",
       "      <td>40.9</td>\n",
       "      <td>0.16305</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date  energy_consumed  no_of_evs_charging  public_holiday_or_event  \\\n",
       "0 2024-01-01        17.460563            7.355932                        1   \n",
       "1 2024-01-02        12.471830            5.254237                        0   \n",
       "2 2024-01-03        12.471830            5.254237                        0   \n",
       "\n",
       "   charging_density  precipitation  wind_speed  traffic_flow  usage_rate  \n",
       "0          1.527577            0.0         9.5          40.9     0.22827  \n",
       "1          1.527577            0.2        15.1          40.9     0.16305  \n",
       "2          1.527577            2.2        21.7          40.9     0.16305  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5['usage_rate']=0.0041*df5['energy_consumed']+0.0213*df5['no_of_evs_charging']\n",
    "+0.00003747*df5['traffic_flow']+ 0.0008*df5['wind_speed']+ 0.002*df5['precipitation']\n",
    "+ 0.0094*df5['charging_density']+0.0085*df5['public_holiday_or_event']\n",
    "df5.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4f817b0-6dba-4eab-b49c-237d64317ddc",
   "metadata": {},
   "source": [
    "For the visualization of annual charging pile utilization rate predictions, considering the significant differences between workdays and holidays, this report adopts a dual-panel time-series visualization method with a shared y-axis to simultaneously present the divergent trends. The left panel displays the continuous variation in workday utilization rates across the full 2024 cycle (n=261 days), while the right panel illustrates the dynamic patterns of holiday utilization rates at discrete time points (n=105 days)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "79e9bee7-7ed1-4fb6-8245-f2299e2de05b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vstzkXAWZM+96MjMz1a9fP6Wnp+f6+f7666+aPHmyxowZoyZNmthdd84x5hwzAAAAcCfi9p4AAADALbjnnntsxnICxIsXL1qNb9++XXPnztW+fft0/vx5ZWVlWbZd73Z9jRo1shn74YcfJElNmza12RYQEKC6detq27Ztdh9DsWLFVLFixVyP45+31Tt+/LjS09PVoEEDm3pNJpOaNGmiY8eO2f2+0tVbKOY8K9HT01MhISHq3r27xo0bl+uzwG7m22+/lXQ1zM3t+XY59R07dswq0ChIvXr10ttvv63atWsrOjparVq1UpMmTRQYGGgz9/Dhw5o5c6Z2796t+Ph4q5BPuhqK5oTKP/zwgwIDA3X33XfbrNO0aVMtXLjQaiznszh27Fiun0V8fLzMZrN+/fVXNWjQwK5je+2112zG5s6dqzFjxtiMZ2Rk6M0339Tq1at17NgxXb582eq2rWfPnrXrPaX/fywbNmzINSz09vbOc+/dTMWKFRUSElJgtdj7d0XOz3du/d+kSZNcb4d5PYmJiZKk4sWL2zV/0aJFmjFjhqKiojRv3jy738deZrNZgwcP1s6dOzVs2DD169fPZvvAgQNVtmxZvfDCC3laOyeAzXkOKAAAAHAnIvQDAADAHa106dI6duyYzpw5o2rVquV5/9yuesn5R/ns7GzL2Icffqjo6GgVKVJEHTt2VGRkpAICAmQymbR8+XKdOnUq1/Vzu0In58qs0NBQu/fJ6zHkHIfZbC6095WkatWqFWhYc+HCBUnSl19+qS+//PK681JSUm66Vs7VdP/8DK6Vs+2fV969/vrrqlSpkpYvX64XXnhBL7zwgvz8/NSrVy+99tprliBpz549uu+++yRJHTp00F133aUiRYrIZDJp7dq1+uGHH5Senm5ZNzk5WeHh4bnWkdtnn/NZvP/++zc8Tns+ixxxcXEqXbq0rly5on379mnIkCH6z3/+o+rVq9s8R61Hjx76/PPPVbVqVUVHR6tUqVLy9vbWxYsXNW/ePKtju5mcY8nrlW634nr9nN9a7P27IudKuVKlStnM9/T0VMmSJe1+T39/f0nSlStXbjp32bJlGjFihOrUqaNNmzapSJEiudZ/vSv5cv5+uN7fJ4ZhaNiwYXrvvffUt29fvf322zZzXn/9dX377bfaunWrAgICblrzP+UcY173AwAAANwJoR8AAADuaM2aNdP27du1ZcsWSwBTGKZNmyY/Pz8dOHBAd911l9W21atXX3c/k8lkM5ZzG72EhIRc9/nrr79uodLrc9T75kVOjW+88YYef/zxW1orJ7xITExUpUqVcp2Tc1XRP4MOb29vPf3003r66ad19uxZ7dixQ8uWLdPKlSsVHx+vjRs3SroaGqWnp2v37t1q1qyZ1brffvut5Yqvfx5bXj77nM/i888/1wMPPGDPIdvN399frVu31pdffqm7775bgwcP1m+//WYJXPbv36/PP/9cHTt21Jdffml1C8dvv/02z1eR5RxLcnKyihYtWnAHcgO5/ezdjlpyeuncuXM227Kzs5WYmJjrbWFzkxPQ5wSV1/POO+9o2LBhqlmzprZs2ZJrsBgYGKgyZcro5MmTys7Otrkt52+//SZJNn+/SVfD8aFDh2rZsmV65JFHtHz5cqugPMfhw4dlGIbatGmTa50LFy7UwoUL1a1bN61du9ZqW84xXu+XEgAAAIA7Ac/0AwAAwB1t4MCB8vT01KJFi64bqOTIy5VJ1/rjjz9Uo0YNm38QP3v2rP744488rZXzfLY9e/bYbEtNTbUJiwpKtWrV5OvrqwMHDigjI8Nqm2EYltseOlLjxo0lyeq2pPmV8yzF662VmJio3377TRUqVLju1U1ly5bVI488og0bNuiuu+7S5s2bLVck/fHHHypRooRN4JeamqqDBw/arFW3bl2lpKToxx9/tNmWWy8U5GdxPdWrV9fo0aN19uxZzZ071zKe09NdunSxCYd27dqV61qenp5WV7z9U86xOFOPFVYtOT/fuX1Oe/futbot8M3k9HBOIJebd955R0OHDlX16tW1devWG4ZmrVq1UkpKir755hubbTlhdqtWrazG/xn4RUdH691337XpiX+uP2TIEJuvzp07S7rab0OGDLF5XqckyzNUr30GKgAAAHAnIfQDAADAHa1KlSoaP368zp8/r06dOunkyZM2c9LS0jR79uxcn4tmr4iICP3+++9WV2SlpaVp5MiRefpH/Jy1mjVrpkOHDumjjz6y2vbqq6/e9Kqe/PL19VWPHj0UHx+v119/3WrbypUrdfTo0UJ537xo1KiRGjdurFWrVmnNmjU2281ms3bs2GHXWn379pWHh4deffVVnT592madp59+WllZWerfv79lPD09XVu3brV6dp109Raaly5dkre3tyXwiIiI0N9//61ffvnFMi87O1v/+c9/cg2gH330UUnSlClTrG45euzYMa1YscJmfrdu3VShQgXNnj1bO3futNmemZmp3bt32/NR3NCECRPk7++vWbNmWW7xGBERIUk26//yyy+aMWNGruuUKFFC58+fV1pams22UaNGycvLS0888YT+/PNPm+0XL17UoUOHbvVQ7FLYtXTr1k1BQUF655139Ouvv1rGMzMzNXny5Dyt1aJFC3l4eOi7777LdfvSpUutAr/cbin6T8OHD5ckTZ482Sr437JlizZu3KiWLVuqatWqlnGz2awhQ4Zo2bJl6tmzp957773rBn6SNGjQIC1ZssTm6+mnn5Z0NRRcsmSJRo8ebbPvvn37LHMAAACAOxW39wQAAMAd74UXXlBaWprmzJmjatWq6b777lPt2rXl7e2tkydPavPmzUpMTNQLL7yQ7/d44okn9MQTTygqKko9evRQVlaWNm3aJMMwVLdu3TxfnffGG2+oZcuW6t27tx5++GFVrlxZBw8e1LfffquWLVtq586dud4+71bNmDFDmzdv1tNPP61t27apXr16On78uL744gvdf//92rBhQ6G8b16sWrVKbdq0Ue/evTV37lzVr19ffn5+io2N1d69e5WQkJBrsHStqlWratasWfr3v/+tmjVrqlu3boqIiFBycrI2bdqkY8eOqUmTJnr22Wct+1y5ckVt27ZVpUqV1LhxY1WoUEGXL1/WF198ofj4eD3zzDPy8fGRdLUnvv76azVv3ly9evWSn5+ftm/frjNnzqh169bavn27VT2DBg3Su+++q88++0z169dXx44ddeHCBa1evVrt27fX559/bvXZ+/r66qOPPlKnTp3UqlUrtW3bVrVr15YkxcbGateuXSpZsuQtP1MxLCxMI0eO1OzZszVnzhxNnTpVjRo1UqNGjfS///1PcXFxuvfeexUbG6vPPvtMXbp0sQmrJem+++7T999/rwcffFAtWrSQj4+PmjdvrubNm6t27dqaP3++Ro4cqWrVqqlz586qXLmykpOTdeLECe3YsUMDBw7M9TlxBa2wawkODtbrr7+ugQMHqmHDhurdu7eCg4P1xRdfyN/fX2XKlLF7reLFi6tly5batWuX0tPT5evra9m2detWDRs2TIZhqGXLllqwYIHN/vXq1VP37t0tr9u0aaOhQ4dqyZIlioqKUpcuXfTXX39pzZo1CgoKsllj+vTpWr58uYoUKaKqVavm+ndo9+7dVa9ePbuPKTeGYWjLli2qUaOGVegIAAAA3HEMAAAAAIZhGMb+/fuNwYMHG1WqVDH8/f0NX19fIzIy0njkkUeMr7/+2mrugAEDDEnGyZMnbdaZOnWqIcnYtm2bZcxsNhtvv/22UatWLcPPz88oXbq0MWTIEOOvv/4yWrVqZVz7f81zW+Nahw4dMjp27GgUKVLEKFq0qNGpUyfjp59+Mh544AFDkvH3339b5m7bts2QZEydOtVqjYiICCMiIiLX9XOryzAM48SJE0bPnj2N4OBgIyAgwGjRooWxY8cO4/HHHzckGYcOHbpuzf8kyahWrZpdc3Or5XrHZBiGceHCBWPy5MlG7dq1DX9/f6NIkSLGXXfdZfTp08f45JNP7HrPHFu3bjW6d+9ulC5d2vDy8jKCgoKMRo0aGa+99pqRlpZmNTcjI8N45ZVXjA4dOhjly5c3fHx8jLCwMKNVq1bG6tWrbdb+6KOPjHvuuccICAgwQkJCjF69ehl//PHHdfvr8uXLxr///W+jbNmyhq+vr1GzZk1j0aJFxkcffWRIMubMmWPzHqdPnzbGjBlj3HXXXYavr68RFBRk1KhRwxg6dKixZcsWuz6DnM8/Li4u1+3x8fFGQECAERwcbFy4cMEwDMM4d+6cMXjwYKNs2bKGn5+fUadOHeOtt94yTpw4YUgyBgwYYLXGpUuXjGHDhhllypQxPDw8cv3efvfdd0bv3r2NsmXLGt7e3kZISIhxzz33GBMmTDCOHj1q17H8U0RERK49Lslo1arVDfe1t5Yb9enJkydz/SwMwzA+/fRTo379+oavr69RqlQpY+jQocaFCxdu+DObm1WrVhmSjI8//thqfNmyZYakG37lVld2drbx+uuvG7Vq1TJ8fX2NkiVLGj169DCOHz9uMzenj2/0tWzZspseQ85n+Nhjj+W6ffv27YYkY+7cuXZ9JgAAAIC7MhnGNfedAQAAAOCysrOzVblyZV25csXqVqK3Q/PmzbV3714lJSWpSJEit/W973STJ0/Wiy++qPXr16tTp06OLgdOJCMjQ1WrVlWNGjX01VdfObqcQtG/f3998cUXOnHihIoVK+bocgAAAACH4Zl+AAAAgAvKysrS+fPnbcZffvllnTp1yuqWfAUtLi7OZuz999/XN998o3bt2hH4FaLcPvsjR47o9ddfV7FixXieGWz4+PjopZde0oYNGyzPvXMnv//+uz744ANNmTKFwA8AAAB3PJ7pBwAAALigy5cvq1y5cmrfvr2qVq2qzMxM7du3T/v371eZMmU0bdq0Qnvv2rVrKyoqSjVr1pSnp6cOHz6s7du3q2jRopo1a1a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",
      "text/plain": [
       "<Figure size 1800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1589583838057552\n",
      "0.1771533303386692\n"
     ]
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "df5['date'] = pd.to_datetime(df5['date']) \n",
    "workdays = df5[df5['public_holiday_or_event'] == 0].sort_values('date')\n",
    "holidays = df5[df5['public_holiday_or_event'] == 1].sort_values('date')\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(18, 6), sharey=True)\n",
    "fig.suptitle('Charging Pile Usage Rate Trend (2024)', fontsize=14)\n",
    "\n",
    "sns.lineplot(data=workdays, x='date', y='usage_rate', \n",
    "            ax=ax1, color='#1f77b4', linewidth=1.5)\n",
    "ax1.set_title('workdays', pad=12)\n",
    "ax1.set_xlabel('Date')\n",
    "ax1.set_ylabel('usage_rate (%)')\n",
    "ax1.grid(alpha=0.3)\n",
    "\n",
    "sns.lineplot(data=holidays, x='date', y='usage_rate', \n",
    "            ax=ax2, color='#d62728', linewidth=1.5)\n",
    "ax2.set_title('holidays', pad=12)\n",
    "ax2.set_xlabel('Date')\n",
    "ax2.grid(alpha=0.3)\n",
    "\n",
    "for ax in [ax1, ax2]:\n",
    "    ax.xaxis.set_major_locator(plt.MaxNLocator(6)) \n",
    "    plt.setp(ax.get_xticklabels(), rotation=30)    \n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "print(df5['usage_rate'].median())\n",
    "print(df5['usage_rate'].mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8d2bb4f-f454-4915-9c5c-62e42565fec7",
   "metadata": {},
   "source": [
    "## 3.Research Findings"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1cf0908-178d-405a-9afc-0f0527924e6c",
   "metadata": {},
   "source": [
    "Based on time-series analysis and regression modeling results, this report preliminarily concludes the following regarding Shanghai's charging pile operations in 2024:\n",
    "\n",
    "1.Supply-Demand Balance Achieved\n",
    "The charging infrastructure construction scale has reached baseline equilibrium with the new energy vehicle ownership in Shanghai. This is specifically manifested in daily utilization rates fluctuating between 15% and 23%, which essentially covers the reasonable operational range (15%-30%) defined by the China Electric Vehicle Charging Infrastructure Promotion Alliance (EVCIPA, 2024). This further demonstrates that the scale expansion phase objectives of charging infrastructure construction have been fundamentally achieved (Shanghai Municipal Transportation Commission, 2025).\n",
    "\n",
    "2.Structural Optimization Needs in Resource Allocation\n",
    "The median daily utilization rate (15.8%) approaching the lower limit of the reasonable range (15%) reflects current efficiency disparities in charging pile distribution. According to research conclusions from Beijing Institute of Technology's (2024) \"New Charging Network Evolution Model,\" it is necessary to implement precision deployment strategies to improve resource utilization efficiency in low-performance areas.\n",
    "\n",
    "3.Reliable Model Validation Results\n",
    "The regression model's predicted annual average utilization rate of 17.7% shows only a 0.1 percentage-point absolute error compared to the official 17.6% figure released by Shanghai Municipal Transportation Commission (2025), with an error rate below the industry acceptable standard (±1%). This confirms the model's strong explanatory power for macro trends."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dacf7a1d-8570-464d-b7d8-cf3c46ee1828",
   "metadata": {},
   "source": [
    "In summary, this study has achieved methodological innovation in charging infrastructure evaluation through a tripartite progressive approach: initially validating that the citywide average utilization rate (15%-23%) meets industry benchmarks (Finding 1), subsequently revealing structural imbalances concealed beneath apparent adequacy via median analysis (15.8%, Finding 2), and ultimately developing a predictive model with merely 0.1% error margin (Finding 3), thereby transcending limitations of static standards. This integrated \"macro-validation, micro-diagnosis, dynamic-prediction\" research framework not only confirms quantitative achievement of policy targets, but more significantly delivers diagnostic tools for identifying inefficiency hotspots and a decision-support system for precision regulation, providing crucial instruments for transforming charging network development from \"scale expansion\" to \"efficiency enhancement."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1f3ecc2-6c51-4256-a1c8-69bc6496be69",
   "metadata": {},
   "source": [
    "To further enhance the visibility of results, an interactive visualization tool has been developed. You can dynamically explore charging pile data by selecting time ranges (full, q1, q2, q3, q4) or metrics (usage_rate, energy_consumed, no_of_evs_charging). This tool allows intuitive observation of trends across different periods, complementing the analytical findings above. The implementation code is as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "43770cab-3e08-4a4a-8469-2a15f1d48d33",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Select time range and metric, then run the function below:\n",
      "\n",
      "Time Range Options: full, q1, q2, q3, q4\n",
      "Metric Options: usage_rate, energy_consumed, no_of_evs_charging\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.dates as mdates\n",
    "from IPython.display import display, clear_output\n",
    "\n",
    "def plot_chart(time_range, metric):\n",
    "    time_ranges = {\n",
    "        'full': (df5['date'].min(), df5['date'].max()),\n",
    "        'q1': (pd.Timestamp('2024-01-01'), pd.Timestamp('2024-03-31')),\n",
    "        'q2': (pd.Timestamp('2024-04-01'), pd.Timestamp('2024-06-30')),\n",
    "        'q3': (pd.Timestamp('2024-07-01'), pd.Timestamp('2024-09-30')),\n",
    "        'q4': (pd.Timestamp('2024-10-01'), pd.Timestamp('2024-12-31'))\n",
    "    }\n",
    "    \n",
    "    metric_labels = {\n",
    "        'usage_rate': 'Usage Rate (%)',\n",
    "        'energy_consumed': 'Energy Consumed',\n",
    "        'no_of_evs_charging': 'Number of EVs Charging'\n",
    "    }\n",
    "    \n",
    "    start_date, end_date = time_ranges[time_range]\n",
    "    filtered_df = df5[(df5['date'] >= start_date) & (df5['date'] <= end_date)]\n",
    "    \n",
    "    clear_output(wait=True)\n",
    "    print(f\"Current View: {time_range} - {metric_labels[metric]}\")\n",
    "    \n",
    "    plt.figure(figsize=(12, 6))\n",
    "    plt.plot(filtered_df['date'], filtered_df[metric], color='#2563eb', linewidth=2)\n",
    "    \n",
    "    plt.title(f'Shanghai Charging Pile {metric_labels[metric]} Time Series Analysis 2024', fontsize=14)\n",
    "    plt.xlabel('Date', fontsize=12)\n",
    "    plt.ylabel(metric_labels[metric], fontsize=12)\n",
    "    \n",
    "    if (end_date - start_date).days > 90:\n",
    "        plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%m-%d'))\n",
    "        plt.gca().xaxis.set_major_locator(mdates.MonthLocator())\n",
    "    else:\n",
    "        plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%m-%d'))\n",
    "        plt.gca().xaxis.set_major_locator(mdates.WeekdayLocator(interval=1))\n",
    "    \n",
    "    plt.xticks(rotation=45)\n",
    "    plt.grid(True, linestyle='--', alpha=0.7)\n",
    "    plt.legend([metric_labels[metric]])\n",
    "    \n",
    "    stats = filtered_df[metric].describe()\n",
    "    stats_text = (f\"Statistics:\\n\"\n",
    "                 f\"Mean: {stats['mean']:.2f}\\n\"\n",
    "                 f\"Min: {stats['min']:.2f}\\n\"\n",
    "                 f\"Max: {stats['max']:.2f}\\n\"\n",
    "                 f\"Std Dev: {stats['std']:.2f}\")\n",
    "    \n",
    "    plt.figtext(0.02, 0.02, stats_text, fontsize=10, bbox=dict(facecolor='white', alpha=0.5))\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "current_time_range = 'full'\n",
    "current_metric = 'usage_rate'\n",
    "\n",
    "print(\"Select time range and metric, then run the function below:\")\n",
    "print(\"\\nTime Range Options: full, q1, q2, q3, q4\")\n",
    "print(\"Metric Options: usage_rate, energy_consumed, no_of_evs_charging\")\n",
    "# Example:plot_chart('full','usage_rate')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5886f41c-f8fe-4a28-b935-f17a3e2cda5a",
   "metadata": {},
   "source": [
    "## 4.Limitations and Improvement Strategies"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c8b9397-411b-40c7-b5a0-94ee56f9cd4e",
   "metadata": {},
   "source": [
    "The primary limitation of this research is its inability to address regional variations, resulting from the use of simplified input data—namely per-charging-event averages (for energy consumption and vehicle counts) and city-wide averages (for charging station density)—due to constraints in obtaining high-resolution datasets."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cda5292a-fc07-47d5-a584-05d617712eee",
   "metadata": {},
   "source": [
    "Consequently, the model's outputs only reflect Shanghai's average utilization rate (17.6%). This insufficient spatial resolution restricts the applicability of findings to macro-level supply adequacy assessments, while rendering them ineffective for guiding localized infrastructure planning decisions."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aff05e91-09c7-4c77-b920-658fb791c151",
   "metadata": {},
   "source": [
    "Taking charging station density as an example,this core input parameter in our regression model demonstrates severe spatial aggregation bias when using citywide averages. To demonstrate this phenomenon,we first obtain the geographical coordinates of charging stations through Baidu Maps API using stratified random sampling. Density heatmaps are subsequently generated, covering three representative zones: (1) Huangpu District (historic commercial core), (2) Pudong New Area (modern urban center), and (3) Chongming District (peri-urban reference). This tripartite spatial framework ensures comprehensive coverage of Shanghai's charging infrastructure gradients."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "65eeb797-fef0-4855-aa72-6264a8ffbf6a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scraping 上海市 data...\n",
      "Data saved to charging_pile_data.csv\n"
     ]
    }
   ],
   "source": [
    "from time import sleep\n",
    "\n",
    "class ChargingPileCrawler:\n",
    "    def __init__(self, ak):\n",
    "        self.ak = \"Lp2j16YLpjQJmSvGAP4gXJOvq206rlhn\"\n",
    "        self.base_url = \"http://api.map.baidu.com/place/v2/search\"\n",
    "        self.headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64)'}\n",
    "        \n",
    "    def get_city_data(self, city, max_pages=10):\n",
    "        page_num = 0\n",
    "        results = []\n",
    "        \n",
    "        while page_num < max_pages:\n",
    "            params = {\n",
    "                \"query\": \"充电桩\",\n",
    "                \"region\": city,\n",
    "                \"output\": \"json\",\n",
    "                \"ak\": self.ak,\n",
    "                \"scope\": 2,  \n",
    "                \"page_size\": 20,\n",
    "                \"page_num\": page_num\n",
    "            }\n",
    "            \n",
    "            try:\n",
    "                response = requests.get(self.base_url, params=params, headers=self.headers)\n",
    "                data = response.json()\n",
    "                \n",
    "                if data.get('status') != 0:\n",
    "                    print(f\"failed：{data.get('message')}\")\n",
    "                    break\n",
    "                    \n",
    "                results.extend(data.get('results', []))\n",
    "                page_num += 1\n",
    "                sleep(1)  \n",
    "                \n",
    "            except Exception as e:\n",
    "                print(f\"Request failed：{str(e)}\")\n",
    "                break\n",
    "                \n",
    "        return pd.DataFrame(self._parse_data(results))\n",
    "\n",
    "    def _parse_data(self, raw_data):\n",
    "        parsed = []\n",
    "        for item in raw_data:\n",
    "            parsed.append({\n",
    "                \"name\": item.get('name'),\n",
    "                \"address\": item.get('address'),\n",
    "                \"longitude\": item.get('location', {}).get('lng'),\n",
    "                \"latitude\": item.get('location', {}).get('lat'),\n",
    "                \"phone\": item.get('telephone'),\n",
    "                \"type\": \";\".join(item.get('detail_info', {}).get('type', [])),\n",
    "                \"num_piles\": item.get('detail_info', {}).get('detail')\n",
    "            })\n",
    "        return parsed\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    crawler = ChargingPileCrawler(ak=\"Lp2j16YLpjQJmSvGAP4gXJOvq206rlhn\")\n",
    "    \n",
    "\n",
    "    cities = [\"上海市\"]\n",
    "    \n",
    "    all_data = pd.DataFrame()\n",
    "    for city in cities:\n",
    "        print(f\"Scraping {city} data...\")\n",
    "        city_df = crawler.get_city_data(city)\n",
    "        all_data = pd.concat([all_data, city_df])\n",
    "        sleep(2)  \n",
    "        \n",
    "    all_data.to_csv(\"charging_pile_data.csv\", index=False, encoding='utf_8_sig')\n",
    "    print(\"Data saved to charging_pile_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ac402dfe-0884-412e-847d-a6cc18d4eb3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>address</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>phone</th>\n",
       "      <th>type</th>\n",
       "      <th>num_piles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>国家电网充电站(上海浦东新区金京路人才公寓电动汽车站)</td>\n",
       "      <td>上海市浦东新区俱进路555号</td>\n",
       "      <td>121.622475</td>\n",
       "      <td>31.306801</td>\n",
       "      <td>40001855224000798720</td>\n",
       "      <td>l;i;f;e</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>小桔充电充电站(清帆能源两港装饰城超充站(二期))</td>\n",
       "      <td>宣桥镇南六公路6752港上海两港装饰城</td>\n",
       "      <td>121.717976</td>\n",
       "      <td>31.078190</td>\n",
       "      <td>4000610999</td>\n",
       "      <td>l;i;f;e</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>小桔充电充电站(宝丰联大酒店快充站)</td>\n",
       "      <td>宝丰联大酒店地下停车场B1层</td>\n",
       "      <td>121.493759</td>\n",
       "      <td>31.303232</td>\n",
       "      <td>4000610999</td>\n",
       "      <td>l;i;f;e</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          name              address   longitude   latitude  \\\n",
       "0  国家电网充电站(上海浦东新区金京路人才公寓电动汽车站)       上海市浦东新区俱进路555号  121.622475  31.306801   \n",
       "1    小桔充电充电站(清帆能源两港装饰城超充站(二期))  宣桥镇南六公路6752港上海两港装饰城  121.717976  31.078190   \n",
       "2           小桔充电充电站(宝丰联大酒店快充站)       宝丰联大酒店地下停车场B1层  121.493759  31.303232   \n",
       "\n",
       "                  phone     type  num_piles  \n",
       "0  40001855224000798720  l;i;f;e        NaN  \n",
       "1            4000610999  l;i;f;e        NaN  \n",
       "2            4000610999  l;i;f;e        NaN  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url6=\"charging_pile_data.xlsx\"\n",
    "df6=pd.read_excel(url6)\n",
    "df6.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e564c24d-1f03-4bd1-ae8c-6b22d762482e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Skipping field center: unsupported OGR type: 3\n",
      "Skipping field centroid: unsupported OGR type: 3\n",
      "Skipping field acroutes: unsupported OGR type: 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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+2HPdwYMHxXnz5okAxFtuuWXAdu3t7WJkZKQIQLzssss8ryGXyyU+9dRTniBlIoFJS0uLJ4hKTU0VX3zxRbGhoWFc2470edLfZJ6TkR63/kZ6v+zcudPzpcFf/vIXz++Gnp4e8dprrxUBiMHBweKBAwcGbNf/99fatWs9z73L5RJvueUWEYAYHR094H3y1ltviQDE+fPni/v27Ruwv4aGBvGxxx4Tu7q6RrwPRETkHQxMiGaRiQYmAMS33357yPXuk70zzzxz2MvPOeecYfe7a9cuURAEMTk5ecDl7j+MIyMjPSeVg7n/4Fy4cOGQDpXS0lJPvWedddaQbT/99FMRgDhv3rzR7vYQF198sQhA/O9//zvg8pkITM4++2wRgHjjjTd6Ltu1a5fn2+LBJwGiKIpdXV1iXFycKAiCWFNTM6ReQRAGdB+43XTTTZ6Qy+3gwYOex3u8JhuYiKIo/uxnPxMBDAlnRFEUzzzzzGGDttEMDkz+9a9/iQDEa665xnObjRs3igDEJ554QhTFkQOTscTFxYlBQUGiw+EYcLn7NRseHu45QerPHXCtWbNm3Mdavnz5iF0bXV1dnm/X+38LL4reCUzG+tm5c+eA7axWq6db69NPPx2y36ysLBGA+D//8z/jrqWgoMBzvP6B7ES4tx+uo+nHH3/0nPROhLsLZHBHg/t1CEDcvn37sNu6uwjuvffeIdc1Nzd7uhcmGpgAw3cwvf/++8N+Hj777LMiADEjI2PIa1kUf3p/TyQwEcW+UFoulw94rSQlJYmrV68Wn3/++WE/y/ofb7TAZDQjPSdTCUwuuuiiEX/PuFwuz5cCd9xxx4Dr3O+h+fPnDwng7Xa7J6jasWOH5/IHHnhABCbfSUVERN7BOUyIaEQhISH4xS9+MeTyRYsWAcCQcfDvvPMOAODKK68cdn/z5s1DYmIiqqqqsH///iHX//KXvxxz4j/3ePb+5syZ41mC94orrhiyzcKFC4et162kpAR33XUXfvGLX2DFihU4+uijcfTRR+Prr78GAOzatWvUmqaD+3Ho6OjwXPbuu+8C6Ft+1WAwDNkmKCgIJ554IkRRxLfffjvk+gULFiA/P3/I5cM9nxaLBWq1GmVlZTNy/y+//HIA8Mxb4NbY2IhPPvkEKpUKF1xwwaT3f+qpp8JsNuPNN9/0jPt/5ZVXoFAosHr16nHt4z//+Q9uvPFGnHbaaTjmmGM8r5O2tjZ0d3ejvLx82O2uuOIKaDSaIZe7V+357LPPxnX8zs5ObNq0CQBw/fXXD7k+KCgIv/71rwEAn3/++bj2ORFxcXFYvnz5iD/9l4QFAI1Gg1WrVgEYOn/Mjh07UFRUBKVSifPPP3/cNfR/P0x1ktDhPqfmzp0LjUaDtrY2NDU1Dbm+trYWGzduxHnnnYfjjz/e8xp44403AIz8WZGdnY3c3Nwhl3d0dOCHH34AgGEnpw0JCZnSUtzDfR6O9Pn9xRdfAOibsFehGLqI4mQnz12zZg22bt2K888/3/M5XV1djddffx1XXHEFkpKS8M9//nNS+wYm/5xMhvt9Ndz7TxAE/Pa3vx1wu8Euv/xyyGQD//RWKpWYP38+gIHPSVxcHIC++YqGm8uKiIhmBpcVJqIRpaSkDHt5eHg4gL4TuP52794NALjzzjtHXOHFPUFmXV3dkEkAMzMzJ12T2WxGbW3tsNdbLJZh6wX6lsK9/fbbh52M1q25uXnMurzNXavRaPRc5n5833333REnmdy7dy+Avsd3sIk8n3K5HL/97W/x4IMPIjc3F8uXL8dxxx2Hn/3sZzj66KOHDQCm4txzz8Vvf/tbfPTRRzh8+DDMZjMA4B//+AccDgdWrVqF0NDQSe9fqVTivPPOw9NPP42PP/4YCQkJKCwsxGmnneZ5fYzEbrfj/PPPH3OS1pFeJyO9rt2X19fXo729fcBzPZyKigq4XC6o1WokJycPexv3KiRlZWWj7msyxruscH+XXHIJnn/+ebz33nvo7u72nDC7A5Sf//znCAsLG/f++geFXV1dYz5moxnp/WCxWLBv3z50dnYOqO2ll17C1VdfjZ6enhH3OdHXQEVFBURRRFhYGKKjo4e9zbx580Y83mjMZjOCg4OHXD7S57c78BvpeJOtA+gLrV9//XU4HA7s3LkTW7ZswaefforPP/8cTU1NWL16Ncxms2dC1vGaynMyUa2trZ7JzrOysoa9zVjvv4l8Bp999tlITEzE559/jujoaJxyyin42c9+hhUrVgxZbYiIiKYPO0yIaEQjfYPr/oZMFMUBl7e1tQEAtm/fjv/+97/D/ri/IXYvczqe4/XnPuEazN11Mtz1I63q8M033+DWW2+FIAh44IEHsGfPHnR2dsLlckEURdx2220AIMlKBLW1tQB++kMa+OnxraioGPHxdXfuTOTxHen53LhxIx577DGkpKTg22+/xZ/+9CesXLkSERERWL9+PWw229TvaL/azjvvPDgcDrz22muey90dJ4NXCJkM91K3r7zyiueE3X3ZaDZu3Ij33nsPkZGR+L//+z/U1NSgp6cHYt+wVs9yuCO9Tvo/hyNd3r9zYiTukymLxTLiazoiImLc+5sJxxxzDBISEtDZ2ekJnHp7e/H6668DGN/j319MTIzn/6urq6dU20TeD5WVlfj1r3+Nnp4e/P73v8fOnTvR3t7u+ax47rnnAIz8GhjpWO7VqYbrGHMb7brRjHX/JlrLZOvoT6lUYvHixVi7di0+/PBD7Ny5E9HR0XC5XBNeRn2qz8lE9Q8zRnpPj/X+m8hrTqfT4dtvv8Vll10Gl8uFN954A2vXrkVOTg6ys7Px4YcfTup+EBHRxDAwISKvcbfkl5eXe04mR/oZvBSmFF599VUAwB//+EesW7cOWVlZ0Ol0npPRwUu0zpS2tjZPN8nixYs9l7sf3+eee27Mx3einQDDkclkuOGGG1BWVobq6mq89NJLWL16NXp6erBx40b8/ve/n/Ix+hs8LGf37t3YuXMnIiMjccopp0x5/0uXLkVaWho++OADvPLKKzAajTjzzDPH3M79OnnxxRdxySWXICEhAWq12nP9WK+TkZbg7n/5eE5G3c9/Y2PjkHDLrb6+ftz7mwmCIOCiiy4C8FNXyf/7f/8PBw8ehMlkwumnnz6h/ZnNZqSlpQGAZ8jcTHAP5Vq9ejUeeughLFiwAAaDYcqfFe4T6OG639xmKvwaq5bpqCM7O9sTTG/ZsmVC207XczKS/kPOGhoahr2Nt99/sbGxeOGFF9Dc3IzNmzdj48aNyM/PR1FREc4++2zPcC4iIpo+DEyIyGvcbcqFhYUSVzI+NTU1AICjjjpq2OulmLsEAJ5//nk4nU6EhoZi6dKlnsulfHwTExOxZs0avPbaa3j//fcBAC+88MKoQ5ncRuqGGOyoo45CRkYGtm/fjsLCQrz44osAgIsvvhhyuXzStfd30UUXwWazob6+Hr/85S+h1WrH3Ga010lTU9Oww5/6Ky4uHvXyiIiIcQ0tSU1NhUwmg81mG3E+nj179gDom9fHV6xZswZA3xwZjY2NnuDkvPPOGxA8jZd7zpO//vWv6O3t9V6ho5iuzwr3c3r48GEcPHhw2Nu4w9Pp5n7N/PjjjzNah3t4md1uH3D5WJ8bk31Oxvt5NJjJZPIM3ysqKhr2NtP1/lMoFFiyZAluueUWbN26FatXr0Zvby9eeOEFrx6HiIiGYmBCRF7jniD2iSeeGPEbcF/iPll2fyvY3+effy5JYLJz507ceeedAIAbb7xxwAn9OeecA6Dvm/rhJqScKe4Qx2q1oqWlZczbu+/DcMOEBnNPLPn88897Oju8MRzH7ZJLLsEJJ5yAE044wTNB6lhGe508/PDDY560P//888MOX3r66acBACeddNK46tDr9Z6TwyeffHLI9VarFX/7298AACeffPK49jkT0tPTsWjRIjidTrzwwgueyYsnOhzHbe3atTCZTNizZ4+nO2EkNpsNTzzxxKSO099or4GSkhJ88MEHk9qvwWDAkiVLAMATEPbX1tY25tw53rJy5UoAfZ8vw72mh6tvLC0tLXA6naPexj0fk7tzyG2sz43JPicT+TwazP2+Gu79J4qi5/Lpfv+5P4MPHDgwrcchIiIGJkTkRb/5zW+QnJyML7/8EhdddNGQb0w7Ozvx5ptv4qabbpKowoGOPvpoAH1zVPSfD2Hr1q24/PLLvT6x6Wjq6upwzz334Oijj0ZXVxdWrFiBdevWDbhNfn4+zjvvPDQ1NWHlypXYuXPngOt7e3vx1VdfeboopqKoqAi/+c1vsHXr1gHhl81mw3333QcASEhIGNeEne5vkLdu3Trmag9r1qyBQqHAU089hfr6euTn53t1gsPk5GT8+9//xr///W8sW7ZsXNu4Xye///3vPcMVRFHE//3f/+Ghhx4a83XS1NSEK664wjNHhCiKePrpp/HOO+9ALpdP6P1wyy23AOgLW/7xj394Lu/o6MCaNWvQ2NiIxMTEca/8M1Pc4ciGDRvQ2dmJpKQkz9wvExUREYG///3vkMvl+POf/4wLL7wQpaWlA25jtVrx5ptvYuHChV75Ft79Gnj66adRUFDgubysrAznnnsuVCrVpPftfv7vueeeASf5LS0tWL169ajDdbzpggsuQGRkJIqKigZMpCqKIp555pkBr7fx+vrrr5Geno5HH310SCeW1WrFE088gT//+c8Ahq7C4/7c+Oabb4YN4Cf7nCQlJQHo+4wbabjcSH7/+99DoVDgX//6Fx5++GFPh53dbscNN9yAwsJCBAcH45prrpnQfofz6KOP4rHHHhsSCNXW1nqC0eFWXiIiIu9iYEJEXqPX6/HRRx8hKSkJr732GmJjY5GVlYWlS5ciPT0dJpMJ559//ogrvMy0q666CsnJyaisrERGRgbmzZuHjIwMLF68GMHBwZ5lX73t+uuv9yx9mZeXh9jYWMTGxuLOO++EzWbDtddei48//njYpT2ff/55T1iSm5uLhIQELF26FPPmzYPBYMBxxx2Hf/zjH1Pu8LHb7fjrX/+KxYsXIzQ0FHl5ecjNzUVERAQefPBBqFQqPPPMM+PaV25uLtLS0lBdXY34+HgcddRRWLFiBX73u98NuW1kZCROPfVUz7fS3uwumay7774barUa77//PmJiYpCfn4/Y2FhceumlWL16tadDYCR33nkn/vnPfyIqKgqLFi1CbGwsrrvuOoiiiAceeAALFiwYdy2nn3461q1bB4fDgYsuugjx8fFYtGgRoqKi8NZbbyEkJARvvvnmuIYaTdQLL7zged0O93PrrbeOuO3q1auhUCg8J+EXX3zxpIdGAH0riLz33nsICwvDa6+9hoyMDMTHx2Px4sXIzs5GaGgozj//fJSUlODYY4+d9HH6H2/p0qVoaWlBfn4+srKyMHfuXGRkZKCpqQm33377pPe9atUqXH755bBarTjzzDORkpKCRYsWISYmBj/88ANuvvlmAPDasLSRGAwGvPzyy1CpVPjb3/6GyMhILF68GLGxsbj22ms9k7KONGnscARBQFVVFW666SbExsYiLi4OixcvRlZWFsxmM2644QbY7XasXr16yFK955xzDlQqFV5//XUkJSXhmGOOwYoVKzydLpN9TiwWC44//nh0dnYiJSUFS5cuxYoVK8YVMi5YsABPPPEEBEHAH/7wB0RHR2Px4sWIiIjAk08+CbVajVdffRWRkZHjfoxGsnfvXtx4442IjIxEUlISlixZgszMTCQnJ6OwsBA5OTk+8+UDEVEgY2BCRF6VkZGBXbt2YePGjVi0aBHq6upQUFAAu92OY489Fg899JBnhQypGY1GfPfdd1izZg2MRiNKS0tht9tx0003YdOmTdM2cWZhYSH++9//4vvvv0dFRQUUCgV+/vOf495770VVVRX+53/+Z8QTXr1ej08//RSvvvoqTj75ZHR3d2PHjh04fPgw5s2bh1tuuQVbtmyZcndMWloannvuOZx77rmwWCwoKytDeXk5YmJicPXVV6OoqAinnnrquPYlk8nw0UcfYdWqVZDL5diyZQu+/vrrAd8K9+f+plmlUuGCCy6Y0v3whry8PHzzzTdYuXIlXC4XSkpKEB4ejieeeMIzQe1ofvazn+Hbb7/F0UcfjYqKCrS0tGDp0qV455138Mc//nHC9TzwwAP44IMPsHLlSnR2duLHH3+E2WzG1VdfjV27dmHRokWTuZtj2rdv34irM/33v/8dcV4HoO8ktf/EvRdffPGU6zn99NNRVVWFBx98EMcddxzsdjsKCgqwb98+ZGRk4IYbbkBBQQEef/zxKR9LoVDgs88+w/XXX4+IiAhUVFSgtbUVV1xxBbZv3z5g9Z7JeO655/D0009j7ty5qKurw969e3HGGWdgy5YtiIqKAjAzE/meeOKJ2LRpE0477TQAfV0YMTExeO211/Cb3/xmwnWcccYZ2LRpE+68804ce+yxUCqV2L17N6qrqxEeHo5zzz0XH374IV577bUhAXFKSgo++OADHHvssWhpacF3332Hr7/+2jN3yVSek3/84x/41a9+BaPRiO3bt+Prr7/G5s2bx3WfrrnmGnz77bc4++yz4XK5UFBQgKCgIFx88cXYsWOH57GbqquvvhobNmzAMcccA4fDgYKCArS0tGDRokV48sknsWXLlmGXjSYiIu8SRH+YaICIiGaNZ599Ftdccw1WrVqFf/7zn1KXM2krVqzA119/jS+//NInVoUi/3T99dfjqaeewqOPPjpsV9ZM2b59O/Lz8zF//vwRw04iIqJAww4TIiLyKc8//zyAoXMaEM02nZ2deOuttwBg0nO+eMvf//53n6iDiIhoJjEwISIin/H2229j27ZtSE5OHjCEgyiQPfbYY0O6Nurq6vDLX/4Shw4dQm5u7rQNtervyy+/xOuvvz5g0miHw4FHHnkEzzzzDGQy2bhXlyIiIgoEQ2cUJCIimmErVqxAR0eHZ+Wfe++9d0KTSxL5s/feew833ngjgoODkZycDJvNhpKSErhcLpjN5nHNleMNe/fuxWWXXQalUomkpCQYjUaUlZWhvb0dACY8STEREZG/Y2BCRESS+/rrryGXy5GcnIzf//73PjHZK9FMueGGGxASEoIdO3agpKQEoigiJSUFp556Km6++eYpTyo7Xj/72c+wdu1afPnllzhw4ACqqqoQGhqKY489FmvXrsVJJ500I3UQERH5Ck76SkREREREREQ0CPudiYiIiIiIiIgGYWBCRERERERERDQIAxMiIiIiIiIiokEYmBARERERERERDcLAhIiIiIiIiIhoEAYmRERERERERESDMDAhIiIiIiIiIhqEgQkRERERERER0SAMTIiIiIiIiIiIBmFgQkREREREREQ0CAMTIiIiIiIiIqJBGJgQEREREREREQ3CwISIiIiIiIiIaBAGJkREREREREREgzAwISIiIiIiIiIahIEJEREREREREdEgDEyIiIiIiIiIiAZhYEJERERERERE4/bNN9/gjDPOQHR0NARBwHvvvee5zuFw4JZbbsHcuXOh0+kQHR2NNWvW4MCBA2Put7W1Fddddx2ioqKg0WiQmZmJjz/+eBrvyegYmBARERERERHRuHV1dWH+/Pl46qmnhlzX3d2NHTt24I477sCOHTvwzjvvoKysDGeeeeao+7Tb7Vi5ciVqamrw1ltvobS0FM899xxiYmKm626MSRBFUZTs6ERERERERETktwRBwLvvvouzzz57xNts3boVixcvxt69exEfHz/sbZ599lk8+OCDKCkpgVKpnKZqJ0YhdQG+yOVy4cCBAzAYDBAEQepyiIiIiIiIAoooiujo6EB0dDRkssAb+NDT0wO73S51GeMmiuKQc1+1Wg21Wu2V/be1tUEQBJhMphFv8/7772PZsmW47rrr8K9//QsWiwUXXnghbrnlFsjlcq/UMVEMTIZx4MABxMXFSV0GERERERFRQNu3bx9iY2OlLsOrenp6EGkKQZutR+pSxk2v16Ozs3PAZXfddRc2bNgw5X339PRg3bp1uPDCC2E0Gke8XVVVFf7zn//goosuwscff4zy8nJcd911cDqduPPOO6dcx2QwMBmGwWAA0PfmHe0JJSIiIiIioolrb29HXFyc59wrkNjtdrTZevCXladDq/CNoSWjsToduPmLD4ec/3qju8ThcGD16tVwuVx4+umnR72ty+VCeHg4/vrXv0IulyMvLw8HDhzAgw8+yMDEl7hbkYxGIwMTIiIiIiKiaRLIUyBoFUpofWQujvHw9vmvw+HAeeedh+rqavznP/8Zc99RUVFQKpUDht9kZmbi0KFDsNvtUKlUXqttvAJvsBgRERERERERScYdlpSXl+Pf//43wsLCxtxm+fLlqKiogMvl8lxWVlaGqKgoScISgIEJEREREREREU1AZ2cnCgoKUFBQAACorq5GQUEBamtr4XQ6sWrVKmzbtg2vvvoqent7cejQIU+niNuaNWuwfv16z7+vueYaNDU14YYbbkBZWRk++ugj3H///bjuuutm+u55cEgOEREREREREY3btm3bcNxxx3n+fdNNNwEALr30UmzYsAHvv/8+AGDBggUDtvvyyy+xYsUKAEBtbe2AFZLi4uLw+eef48Ybb8S8efMQExODG264Abfccsv03plRMDAhIiIiIiKaIJfL5VfLxs60wXNRUGBZsWIFRFEc8frRrnP76quvhly2bNkybN68eSqleRUDEyIiIiIiogmw2+2orq4eMNcCDWUymRAZGRnQE7tSYGNgQkRERERENE6iKOLgwYOQy+WIi4sbMKSA+oiiiO7ubjQ0NADoW/2EyB8xMCEiIiIiIhonp9OJ7u5uREdHIygoSOpyfJZWqwUANDQ0IDw8nMNzyC8xDiUiIiIiIhqn3t5eAJBsmVN/4g6UHA6HxJUQTQ4DEyIiIiIiognivBxj42NE/o6BCRERERERERHRIAxMiIiIiIiIiIgG4aSvREREREREAe7777/HtddeO+x1p5xyCrZt24bDhw8Pe/2WLVvw7LPP4oUXXhj2+ttvvx2rVq3yWq1EvoKBCRERERERUYBrb2/H2WefjQ0bNgy4vKamBuvWrUNnZycKCgqGbLdixQq4XC4cOHAAjz32GFasWDHg+hdffHHEoIXI33FIDhERERERERHRIAxMiIiIiIiIiIgG4ZAcIiIiIiKiGSaKIsrLy3H48GGYzWakpaVxGV4iH8PAhIiIiIiIaIaVl5fj008/hc1mg1qtBgDMmTNH4qqIqD8OySEiIiIiIpphhw8fhs1mQ0ZGBmw2GydOJfJBDEyIiIiIiIhmmNlshlqtRklJCdRqNcxms9QlEdEgHJJDREREREQ0w9LS0gBgwBwmRORbGJgQERERERHNMEEQMGfOHM5bQuTDOCSHiIiIiIiIiGgQBiZERERERERERINwSA4REREREVGACw4OxocffogPP/xwyHUnn3wyWltbkZ+fP+y2MpkMsbGx+MMf/jDs9bfeeqtXayXyFQxMiIiIiIiIAtyyZcuwbdu2SW+/du1arF271osVEfk+DskhIiIiIiIiIhqEgQkRERERERER0SAMTIiIiIiIiCZIFEWpS/B5LpdL6hKIpoRzmBAREREREY2TUqmEIAhobGyExWKBIAhSl+RzRFGE3W5HY2MjZDIZVCqV1CURTQoDEyIiIiIionGSy+WIjY3F/v37UVNTI3U5Pi0oKAjx8fGQyTiwgfwTAxMiIiIiIqIJ0Ov1SEtLg8PhkLoUnyWXy6FQKNiBQ36NgQkREREREdEEyeVyyOVyqcsgomnE3igiIiIiIiIiokEYmBARERERERERDcLAhIiIiIiIiIhoEAYmRERERERERESDMDAhIiIiIiIiIhqEgQkRERERERER0SAMTIiIiIiIiIiIBlFIXQAR0WwiiiIOHDiAnTt3YseOHdi5cyc6Ozshl8s9txEEATqdDosXL8by5cuRm5sLrVYrYdVERERERLMPAxMiomnU3t6OTZs24ZtvvsGOHTtgt9thsViQlpaG9PR0nHzyydDpdEO26+zsRGFhId566y3cdtttyMvLw80334yIiAgJ7gURERERTZRJIUeQQj72DSWmFl1Sl+CzGJgQEXmR3W7Hxx9/jE8//RTl5eXQarXIycnB/PnzsWrVKiiVynHtR6/XY+nSpVi6dCkAYPv27bj00kuRlJSE9evXIz4+fjrvBhERERHRrMfAhIhoikRRxObNm/Hiiy+irKwMy5Ytw5lnnom4uDgIguCVY+Tl5SEvLw979uzBVVddheXLl2PdunXjDmCIiIiIiGhiGJgQEU1SdXU1XnrpJXz55ZfIzMzEaaedhuuvv35aj5mdnY0HH3wQn332GVauXIk///nPWLJkybQek4iIiIhoNmJgQkQ0Ae3t7Xj99dfx9ttvIzg4GKeccgqefPJJyGQzt+iYIAg45ZRTsHTpUjz00EOIjIzEAw88AL1eP2M1EBEREREFOi4rTEQ0Dg6HA48//jhOP/10tLa24t5778Wdd96JxYsXz2hY0p/JZMJdd92F+fPn45RTTsFHH30kSR1ERERERIGIgQkR0ShEUcS7776LE044AZ2dnXjyySfx85//3KeW+V26dCkeffRRvPvuu7jooovQ2NgodUlERERERH6PgQkR0Qi2bt2KU045BV999RUeeughnHXWWZDLfXNpOK1Wi9/97nc466yzsGrVKrz44osQRVHqsoiIiIiI/BbnMCEiGmTv3r249dZbIYoi1q9fD7PZLHVJ45aVlYXHH38cr776Ks444ww88cQTSE5OlrosIiIiIiK/w8CEiOiIQ4cO4d5770VlZSWuuuoqpKWlSV3SpCgUClx66aXYt28frr76apx44om46aaboFDwI5+IiIiIaLw4JIeIZr36+nrccMMNuPTSS7F06VI8+OCDfhuW9BcXF4eHH34YDocDK1euxI4dO6QuiYiIiIjIb/DrRiKatRobG/HAAw+gsLAQl1xyCX79619LXZLXCYKAs846C8uXL8c999yDlJQU/OlPf0JQUJDUpRERERER+TR2mBDRrFNaWoqrrroKF198MebNm4dHHnkECxculLqsaWU2m3HPPfcgOTkZJ510Ev79739LXRIRERERkU9jhwkRzQqiKOI///kPHn/8cWi1Wpx//vmYM2eO1GXNuGOOOQZ5eXl4+umn8corr+CRRx5BaGio1GUREREREfkcBiZEFNBsNhv+8Y9/4P/+7/+QnZ2Nm266ya9WvZkOOp0Of/zjH7Fr1y6cddZZuPbaa7F69WoIgiB1aUREREREPoNDcogoIB0+fBh33303TjzxROzfvx8PPfQQrr766lkflvQ3f/58PPHEE/jhhx9wzjnnYN++fVKXRERERETkM9hhQkQBpaWlBXfffTeKi4vxy1/+Ek8//TQ7J0ahVCpx5ZVXoqqqCr/61a9w5plnYu3atZDL5VKXRkREREQkKXaYEFFAsNvteOSRR3D22Wdj/vz5ePjhh3HUUUcxLBmn5ORkPProo2hpacHJJ5+MwsJCqUsiIiIiIpIUAxMi8muiKOKtt97C8ccfD7vdjieffBKLFi2Suiy/JJPJsGrVKqxfvx7r1q3D7bffDofDIXVZRERERESSYGBCRH5ry5YtOPnkk/Hdd9/h0Ucfxemnnw6ZjB9rUxUREYGNGzfCZDLh9NNPx8GDB6UuiYiIiIhoxnEOEyLyO3v37sX69esBALfeeisncp0mp5xyCubMmYPzzjsP9913H4455hipSyIiIiIimjEMTIjIb7S1teHee+/F7t27cfXVVyM1NVXqkgJecnIyHnroIdx///344Ycf8Ic//IHzwhARERHRrMDedSLyeQ6HA0888QTOOOMMpKWl4aGHHmJYMoN0Oh3uvfdetLa24owzzsD+/fulLomIiIiIaNoxMCEinyWKIv71r3/hhBNOQHt7O5588kkcddRRUpc1KwmCgAsuuABXXXUVLr74Yrz00ksQRVHqsoiIiIiIpg0DEyLySXa7HZdccgk+//xzPPTQQzj77LMhl8ulLmvWS05OxmOPPYaCggKce+65aGhokLokIiIiIqJpwcCEiHxOV1cXVq1ahcWLF+Oaa65BUFCQ1CVRPwqFAldccQVWr16Ns88+GwcOHJC6JCIiIiIir2NgQkQ+pbm5GWeddRbOOeccHH/88VKXQ6PIyMjAunXr8Otf/xoul0vqcoiIiIiIvIqBCRH5jP379+Pss8/G1VdfjUWLFkldDo1DcnIylixZgo0bN0pdChERERGRVzEwISKfUFJSgvPPPx/r169HRkaG1OXQBJxzzjnYvHkzfvjhB6lLISIiIiLyGgYmRCS5LVu24Ne//jXuvfdexMXFSV0OTZAgCLjlllvw+9//Hh0dHVKXQ0RERETkFQxMiEhSn3/+OW655Rb8+c9/hsVikbocmqTg4GD8+te/xrXXXit1KUREREREXsHAhIgk8/rrr+Oxxx7Dgw8+CKPRKHU5NEV5eXkwGo14+eWXpS6FiIiIiGjKGJgQkSSeeuopvPPOO7j//vuh0WikLoe85Ne//jX+/ve/o7KyUupSiIiIiIimhIEJEc0oURRx1113oaCgAHfccQcUCoXUJZEXKRQKrFu3DldeeSWsVqvU5RARERERTRoDEyKaUY899hiamprwu9/9DoIgSF0OTYPo6GhcdNFFuOyyyyCKotTlEBERERFNCgMTIpoxn3zyCb799lv85je/kboUmmZLly5Feno67rzzTqlLISIiIiKaFAYmRDQjioqK8Oc//xm33XYbO0tmiXPPPReVlZX47LPPpC6FiIiIiGjCGJgQ0bRramrCVVddhQ0bNkCtVktdDs2gG2+8Effeey+ampqkLoWIiIiIaEIYmBDRtHI4HLjoootwww03wGw2S10OzTCtVou1a9fi6quv5nwmRERERORXGJgQ0bQRRRHXXnstTjnlFGRmZkpdDkkkOzsb8fHx+Nvf/iZ1KURERERE48bAhIimzWOPPQadTocTTzxR6lJIYmvWrMHrr7+OsrIyqUshIiIiIhoXBiZENC3cK+JcccUVUpdCPkAul2P9+vW48sor8cYbb0hdDhERERHRmBRSF0BEgce9Is7DDz/MFXHIIzIyEo8//jj+9re/4b333sP//M//IDQ0VOqyiIiIiKZFsEIGncL3exSUou/XKBU+MkTkVVwRh0ajVCpxzTXX4KyzzsLpp58Oq9UqdUlERERERMOSNDB55plnMG/ePBiNRhiNRixbtgyffPKJ5/p33nkHJ598MsxmMwRBQEFBwbj2+9hjjyE9PR1arRZxcXG48cYb0dPTM033gojcHA4HLr74Yq6IQ2PKycnBWWedheeff17qUoiIiIiIhiVpYBIbG4uNGzdi27Zt2LZtG44//nicddZZ2LNnDwCgq6sLy5cvx8aNG8e9z1dffRXr1q3DXXfdheLiYjz//PN44403sH79+um6G0SEn1bEOfnkk7kiDo3LySefjDfeeANOp1PqUoiIiIiIhpB0DpMzzjhjwL/vu+8+PPPMM9i8eTOys7NxySWXAABqamrGvc9NmzZh+fLluPDCCwEAiYmJuOCCC7Blyxav1U1EQ3FFHJoohUKB4447Dq+99prn856IiIiIyFf4zKSvvb29+Oc//4muri4sW7Zs0vs5+uij8corr2DLli1YvHgxqqqq8PHHH+PSSy8dcRubzQabzeb5d3t7e9//dLcBCnHStRDNFg0NDTg6dy6uuGAVwEleaQJuuvoKFBUVQexq5QTBREQ0OwQFS10BEY2T5IHJ7t27sWzZMvT09ECv1+Pdd99FVlbWpPe3evVqNDY24uijj4YoinA6nbjmmmuwbt26Ebd54IEHcPfdd0/6mESzWUdHByoqKvretzzhpQmSyeUIDjahvr4ekZGRUpdDREREROQh+So56enpKCgowObNm3HNNdfg0ksvRVFR0aT399VXX+G+++7D008/jR07duCdd97Bhx9+iHvuuWfEbdavX4+2tjbPz759+yZ9fKLZxG6348cff8Sc9HTI5HKpyyE/I7pcaKivR1tbK+cxISIiIiKfI3mHiUqlQmpqKgAgPz8fW7duxeOPP47//d//ndT+7rjjDlxyySW48sorAQBz585FV1cXrrrqKtx2222QyYZmRGq1msufEk2Qy+XCzp07kZSUBJVKJXU55Gd6e3uxq6AACQkJWLx4MRQKyX8dERERERENIHmHyWCiKA6YT2Siuru7h4QicrkcoihCFDkfCZE3iKKI3bt3w2KxQG8wSF0O+SG5XA6tVovIyEiGJURERETkkyT9K/XWW2/Fqaeeiri4OHR0dOD111/HV199hU8//RQA0NzcjNraWhw4cAAAUFpaCgCIjIz0jHVfs2YNYmJi8MADDwDoW3nnkUcewcKFC7FkyRJUVFTgjjvuwJlnngk5hwwQeUV1dTUUCgXMFovUpZAfi4+PR0lJKRYuXCB1KUREREREQ0gamNTX1+OSSy7BwYMHERwcjHnz5uHTTz/FypUrAQDvv/8+LrvsMs/tV69eDQC46667sGHDBgBAbW3tgI6S22+/HYIg4Pbbb0ddXR0sFgvOOOMM3HfffTN3x4gCWENDA5qampCeni51KeTndHo9envr0N7RASM7lYiIiIjIxwgix6kM0d7ejuDgYLQdrIXRaJS6HCKf0dHRgd27dyMrK4uTvJJX9Fit2Lt3LxYtWiR1KURERDODywoD6HfO1dYWcOdc7vv2/jnnQadUSl3OmLocDpz57psB+VxMlc/NYUJEvsmzIs6cOQxLyGs0Wi0UCiWam5ulLoWIiIiIaAAGJkQ0JqfTie3btyMpORkqrihFXhYfH4eysjJOzE1EREREPoWBCRGNqtflwvbtOxAbGwu9Xi91ORSAlCoV9Ho96uvrpS6FiIiIiMiDgQkRjcjlErFzx05ERIQj2GSSuhwKYDGxsaiqqmKXCRERERH5DAYmRDQsURTx44+7YAoxITQsTOpyKMDJ5XKEhoZi//79UpdCRERERASAgQkRDUMURRQVFUGr1SI8PFzqcmiWiIqKQm1tLXpdLqlLISIiIiJiYEJEQ5WXlwMAoqKjJa6EZhNBJkN0dDSKi4qkLoWIiIiIiIEJEQ1UXV2NbqsVcXFxUpdCs1CY2Qxnby/q6uqkLoWIiIiIZjkGJkTksX9/HZqbm5GclAQIgtTl0CyVlJiI2tpatHd0SF0KEREREc1iDEyICABwqL4eBw7UITUtjWEJSUqQyZA2Zw52//gj7Ha71OUQERER0SzFwISI0NTUhJrqaqSnp0NgWEI+QKVSITk5Gdu2bYPd4ZC6HCIiIiKahRiYEM1ybW1tKC0tRXpGBgQZPxLId+j0eiQmJmL7tm1wMDQhIiIiohnGsyOiWayzsxOFhYXIyMiAXC6XuhyiIfQGA+Lj47Ft2zb02GxSl0NEREREs4hC6gKISBpWqxW7du1Ceno6FEql1OUQjchgNCIhIQE/7toFuVyOxMQkhIaGcPgYEREREU0rBiZEs5DNZseOHTuQlpYGlVotdTlEY9IbDMjIzIStpwd1dXUoLS1FdEw0YmNioFDwVxkREREReR//yiSaZRxOJ7Zv347k5BRotFqpyyGaELVGg8SkRIguFxobG7F161bo9XokJSVBr9dLXR4RERERBRAGJkSzSG9vL7Zv346EhHjo9DqpyyGaNEEmQ3hEBMIjItDZ0YHikhJoNRpkZmZyPh4iIiIi8gpO+ko0S7hcLuzYsQPRUVEwGI1Sl0PkNXqDAenp6dDp9fjhhx/Q2tomdUlEREREFADYYUI0C4iiiIKCAoSFmWEKCZG6HKJpERYWBoPBgKqqSjidTqSkpCA0NJSTwxIRERHRpDAwIZoFysrKYLVaER4egd7eXg5ZoIClUqmQmpYGu92Ouro6lJWVwWKxIDIyknOcEBEREdGEMDAhmgUSExMRHByMtrY2HDx4AE6nE3K5HDqdHnq9Djq9HiqVSuoyibxGpVIhMSkJosuF1tZWlJeXw2q1wmg0whIeDq1WC41aDaVSyQ4UIiIiIhoWAxOiWUCtViMyMhKRkZGeyxwOB9rbO9DW1op9tbWw2WwAgKCgIOh0fSFKkFYL8GSS/JggkyEkNBQhoaGAKKKrqwstzc2ot9thtzvgcDogAIAgQEDfe8VstiA83MIQkYiIiGiWY2BCNEsplUqEhYUiLCzUc5nL5UJnZyfa2tpwuLERbW1tiIiMRLjFwuCE/J8gQKfXQzfS0BxRhM1mQ0trKwoKCuByuRAaFobIiAgYDAZ2ohARERHNMgxMiMhDJpPBaDTCaDQiLi4Ovb29qKysRFVVFZJTUqQuj2h6CQLUGo2nG0t0udDW1obq6mp0d3f3BSaCAIiipyMFABISEgZ0bxERERFRYGBgQkQjksvlmDNnDkrLylC3fz9iYmOlLoloxggyGUwhIaOuLCW6XKisqkJzSwsyMzLYhUJEREQUQGRSF0BEvm9OWhrsDgcaGxqkLoXIpwgyGVJTU6FSKrFlyxbPXEBEREREgeybb77BGWecgejoaAiCgPfee2/A9aIoYsOGDYiOjoZWq8WKFSuwZ8+eMff72GOPIT09HVqtFnFxcbjxxhvR09MzTfdibAxMiGhMgiBgbs5cNLe0oLWlRepyiHxOeEQE4uPjsW3bNjQ3N0tdDhEREdG06urqwvz58/HUU08Ne/1f/vIXPPLII3jqqaewdetWREZGYuXKlejo6Bhxn6+++irWrVuHu+66C8XFxXj++efxxhtvYP369dN1N8bEITlENC4ymYDchQuxZcsWKJXKkSfOJJqldHo9MrOyUFFeDrPZjKSkJA7RISIiooB06qmn4tRTTx32OlEU8dhjj+G2227DL37xCwDASy+9hIiICPzjH//Ab37zm2G327RpE5YvX44LL7wQAJCYmIgLLrgAW7ZsmZ47MQ7sMCGicZPL5cjLy0NVdTVsErbGEfkqhUKBjIwM9PT0YMfOnXA6nVKXRERERDQu7e3tA34mO9S4uroahw4dwkknneS5TK1W49hjj8X3338/4nZHH300tm/f7glIqqqq8PHHH+O0006bVB3ewA4TIpoQlUqFBQsWoGDnTmRmZkKhVEpdEpFvEQTExsWhtbUVP2zZguysbJhMwVJXRURERDNMJxegk/tBj4KrryM2Li5uwMV33XUXNmzYMOHdHTp0CAAQEREx4PKIiAjs3bt3xO1Wr16NxsZGHH300RBFEU6nE9dccw3WrVs34Rq8hYEJEU2YLigI2dk5KC4uQmZmFmT+8IuAaIaZTCYEBQWhqqoSdrsdoaGhsISHwxQczKE6RERE5HP27dsHo9Ho+bdarZ7S/gb/vSOK4qh/A3311Ve477778PTTT2PJkiWoqKjADTfcgKioKNxxxx1TqmWyGJgQ0aSYTMFISUlBWVkp0rmcKo1Ai95Jb2uF3IuVSEOlUiE1LQ2iy4W29nbs37cPJcXFUCgUMJvNsISHQ6/TSV0mEREREYxG44DAZLIiIyMB9HWaREVFeS5vaGgY0nXS3x133IFLLrkEV155JQBg7ty56OrqwlVXXYXbbrsNMtnMf0nLwISIJi08PBw2mw1VlVVISUkGGJrMaiOFI1a7Y3L7Uw1/uT8GKYJMBpPJBJPJBABwOp1oa23FnsI9iIqKRHx8vLQFEhEREXlJUlISIiMj8cUXX2DhwoUAALvdjq+//hp//vOfR9yuu7t7SCgil8shiiJEUZzWmkfCwISIpiQuLg7Wnh7s378fsYPGPVLgGxySTDYcGc5w+9KqlAOO6Y/hCdA3OWyY2YzQsDCUlpSgubkZkZGRsFgskMv98z4RERHR7NHZ2YmKigrPv6urq1FQUIDQ0FDEx8fjd7/7He6//36kpaUhLS0N999/P4KCgjwr4ADAmjVrEBMTgwceeAAAcMYZZ+CRRx7BwoULPUNy7rjjDpx55pmS/X3EwISIpiwtNRW7d+9GQ309wkdps6PAMJ0hyVj6HysQwhNBEJCRmYkeaw+am5tQU1MDmUyG8IgIREZEQKPRSF0iERER0RDbtm3Dcccd5/n3TTfdBAC49NJL8eKLL+Lmm2+G1WrFtddei5aWFixZsgSff/45DAaDZ5va2toBHSW33347BEHA7bffjrq6OlgsFpxxxhm47777Zu6ODSKIUvW2+LD29nYEBwej7WCtV8ZwEc0Goihi+/btCA8PhykkROpyaBoMCCdmMCQZD63qp9Wa/DE46a/X6URzSwsaGhoQHx+PmOhoqUsiIiJvCuLKaUC/c662toA753Lft/+36nzolCOMMfYhXQ47TnjrjYB8LqaKS1sQkVcIgoCFCxei7sABdHZ2Sl0OeZk7LLHaHT4XlgAD69Kid0qTzUpNrlDAYrFgTloa6vbvl2zMLhEREdFsx8CEiLxGLpcjLzcX1dXV6LH2SF0OeUH/8MEXg5LB3MGJ1e7w++BEqVLBZDJh2/btcDh8/7EnIiIiCjQMTIjIq1QqFRYuWIjy8jI4eZLn13y9q2QsgdBxEhkVhajIKGzZsgXtHR1Sl0NEREQ0qzAwISKvCwrSIicnByUlpXD1+ueJ6mznT10lowmEoTrGYCPSMzKwp7AQdQcOSF0OERER0azBwISIpkVwcDDS0lJRVlbGORj8TKCEJf0NDk78jUqlQlZWFhobGrC7sBBlZWXo7rZKXRYRERFRQGNgQkTTxmKxIDo6GpWVlQBDE78QiGFJf/7cbSLIZEhOSYHRYIRKrcbOnTs4TIeIiIhoGjEwIaJpFRsbC6PBgH379kldCo0h0MMSN3/vNgkJDUFoaCgyMjKwe/duNDc3S10SERERUUBiYEJE0y4lJQUulwv1hw5JXQqNIdDDkv78OTQB+lbRycrMRGlpKeobGqQuh4iIiCjgMDAhomknCAJycnLQ3t6OFn4b7pP8NTSYKn9fgliuUCAzMwt7a2qwf/9+qcshIiIiCigMTIhoRgiCgAULFuDgwYPo4LwLPmW2DMUZjT93m8jkMqRnZKC+oQFVVVVSl0NEREQUMBiYENGMkcvlyM3LQ01NDXqsXOHDl8zmsMTNn0MTQRCQlpqKzs4uFBcXc2UqIiIiIi9gYEJEM0qlVCJ34UKUl5fDYbdLXc6s54/hwHTy59AEgoDEpERAELBr1y7YHQzBiIiIiKaCgQkRzTitVou5c+eitLQUvb1+eGIaYNhdMpBfhyYAYmJiEBoaim1bt6KiooLvMSIiIqJJYmBCRJIwGo2YM2cOSktLIbpcUpdDNED/yWD9kSkkBDk5ORAEAZs3b8bevbVwuThMh4iIiGgiGJgQkWTMZjPi4+JQUVEBcM6FGeevYcBM8ufQBIIAS3g4cnJy0NNjxabNm3Dw4EHOb0JEREQ0TgxMiEhS0dHRCAkJwd69e6UuZVbicJyx+XVoAkCQyRAVHY2srCw0Nzdj8+bNqK+vh4udXURERESjYmBCRJJLTk6GXC7HwQMHpC6FaFj+HpoAfatUxcXHIz09HU3Nzfjhhx9QUFCAw4cPs+uEiIiIaBgMTIjIJ2RmZqK7uxtNhw9LXcqs4O8n/1IIhNAEABRKJWJjY5Gdk4Oo6Ggcqq/Hpk2bsHv3bjS3tAwbnoiiiF52pBAREdEso5C6ACIiABAEAfPnz8e2bdugVCphDA6WuqSAx+E4E2e1O6BVAVbIpS7FK7RaLeLj4wEAXZ1d2L9vP4qLipCeng6z2ey53Y8//ghrTw9cvS5YLBbExMQgKEgrVdlEREREM4KBCRH5DJlMhtzcXGzZshXJyQoE6XRSl0Q0RKCFJm46vQ46vQ69vb0oLSnBgQMHkJiUBINej66ubuTMzYHocqG1tRXFxUWwOxyICA9HbGwsVCqV1OUTEREReR0DEyLyKQqFArl5udi+bRvS09OhUqulLoloiEANTYC+uU6ysrPR1dmJivJy9PT0ICgoCEDfBLIhoaEICQ2F6HKhqakZ27dvR3BwMFJSUqDm+5WIiIgCCAMTIvI5GrUa8+fPx65du5CZlQWFgh9V5Ju06A3I0AQAdHo9UtPS0Ot0DjuviSCTwWwxw2wxo621FTt37oROr0daaio0Go0EFRMRERF5Fyd9JSKfpNfrkZWVhdLSUoicbJJ8kHsOmECYCHY0coUCCqVy1NsEm0zIys5GiMmEXbt29c15YrXOUIVERERE04OBCRH5rJCQECQlJaGsrAzgsqfkg2ZLaDJexuBgZGZlIcxsxu7du1FQUICu7m6pyyIiIiKaFPa5E5FPi4yIgK2nB1XV1UhOTpa6HKIh+uYzUQb08JyJMhgMyMjMRFdnJ4qKiuDq7UV4eDiioqKgVqths9nQ0dmJjvYOdHR2wG6zISoqClFRUZDL+RgSERGRb2BgQkQ+LyEhAT09Pajbvx8xsbFSl+P3tOjlksJextBkeDq9Hunp6ejt7UVLczMKCwvhcDigUqkQFBQEbVAQoo+EJI2HD2PLli0ICgpCfHw8TCYTBEGQ+i4QERHRLMbAhIj8wpw5c/Djjz+isaEBlvBwqcshGsIdmtBQcrkcZosFZotlxNu4O0y6u7pQu28fiouLER4ejri4OK6+Q0RERJJgYEJEfkEQBMydOw/bd2yHUqmEKSRE6pKIhgjk5YZnSpBOh6SkJIguF5pbWrBr1y4IgoDY2DhERIRDJuP0a0RERDQzGJgQkd+QyQTkLlyILVu2QKlUQqfXS10S0bA4NGfqBJkMYWFhCAsLg8NuR0NDA6qrq2AMDkZCfDwMBoPUJRIREVGA49c0RORX5HI58vLyUFVVhR5rj9TlEA3BlXO8T6lSISY2Fjk5OQgJCUFFRQU2bdqEmpoaOBycj4eIiIimBwMTIvI7KpUKCxcuRHl5GZw8WSIfxNBkmggCDAYDUlJTkZmZhd7eXmzfsQPbt29HY2MjRC4/TkRERF7EwISI/FJQUBBycnJQUlICVy9PSifCCjknJ50BXIloesnkMljCw5GVlYX4+HjUNzTg+02bUFJSgu7ubqnLIyIiogDAOUyIyG8FBwcjLS0NpaVlyMhIh8DJIMkHcT6T6afWaBAfHw+IItra2lBUVAxnrxMx0dGIjo6GXM7Hn4iIiCaOgQkR+TWLxQKbzY7KykqkpqYCgiB1SRRARFFEZXUNmppbEBYagpSkRAgTeI25lxpmaDJDBAHBJhOCTSb0Op1oPHwYW7ZsgVarRXx8PEJCQib0/BEREdHsxsCEiPxebGwMenqsqN23r+9bZiIvqayuwRdffQOH3Q6lSgUASE1OmtA+GJpIQ65QIDIyEpGRkbB2d2P//v0oKSmBxRKOuLhYaDQaqUskIiIiH8fAhIgCQmpqKgoLC3Ho0CFERkZKXQ4FiKbmFjjsdqSlJKO8sgpNzS0TDkyAn0ITkoY2KAiJSUkQRREtzc34cfduAEBsbCwiIyIg43A+IiKaBlqZDEF+8DvG5Qc1SoWBCREFjOzsbOzYsQMqpRKhYWFSl0MBICw0BEqVCuWVVVCqVAgLDZnS/thlIi1BEBAaFobQsDA4HQ40NDSgproaBqMRCfHxMBqNUpdIREREPoSBCREFDEEQsGDhQmzbuhVKpRIGnvzQFKUkJQLAgDlMJotDc3yLQqlEdEwMomNi0NnZicrKKvT0WBEZGYmYmBiojgzBIiIiotmLgQkRBRS5TIbc3Fxs3boVKSkp0AYFSV0S+TFBEJCanDSpYTjD4dAc36TX66FP1UN0udDU1IQdO3dCqVAgLi4OFouFE8USERHNUhysREQBR6lUIjc3FxUVFbDb7VKX47N44i4Nq90BLXqlLoOGIchkMFssyMrKQnxCAhobD2PTpk0oLi5GZ1eX1OURERHRDGOHCREFJI1Gg3nz5uHH3buRlZkJuYIfd/1ZIedJu8Q4NMe3qdVqxMXHIU6MRXt7O8pKS2Gz2aDRaGA2m2E2m6HVaqUuk4iIiKYRzyCIKGAZDAZkZmSgtLQUmZmZEDgDOPkIzmfiRwQBxuBgGIODAQA2mw1tra0oKiqC3W6H2h2ghJkRFMQAhYiIKJAwMCGigBYaGoqEhASUl5djzpw5AOciIB/B+Uz8k1qtRnhEBMIjIgAAdpsNrW1tKC4pht1mg1qt9nSgBHEOJSIiIr/GwISIAl5UVBR6bDbU1NQgMck7k3fS9BNFEZXVNQNWqAnEyTfZZeLfVGo1wsPDER4eDuCnAKWkpAS2QQGKVqsNyNcwERFRoGJgQkSzQlJiIoqLi3Ggrg7RMTFSl+MTrJBDq+rrdPBFldU1+OKrb+Cw26E8ssSrt1ar8RUcmhN4hgQodjvaWltRUlKCnh4bNJq+ACXMbEYQAxQiIiKfxsCEiGaNjIwM7Nq1C4cbG2G2WKQuh8bQ1NwCh92OtJRklFdWoam5JeACE4BDcwKdSqWCJTwclkEBSumRAEWtUcMcZobZHIagoCAGKERERD6EgQkRzRqCIGDevHnYtm0blEolgk0mqUuiUYSFhkCpUqG8sgpKlQphoSFSlzSt2GUyO4wYoJSVwdbTA5VKNWAOFAYoRERE0mFgQkSzikwmQ25uLrZu3QqlUokgnU7qkmgEKUmJADBgDpNAxaE5s9fgAMVht6O1rQ1lZWXo6ReghJnN0DFAISIimlEMTIho1lEoFMjNzcW2bdswZ84cqDUaqUuSjC/PYyIIAlKTkwJyGM5wODSHAECpUsFiscByZNigw25HW1sbysvK0dNjhUqlQpjZDHNYGHQ6HQMUIiKiacTAhIhmJbVajQULFqCgoACZmZlQKHmiSr6BXSbUn1Klgtli8cy75LA70NbWioqKClitRwKUsDCYzWYGKERERF7GwISIZi2dTofs7GwUFxcjMzMLMrlM6pJoluPQHBqLUqUcEKA4HQ60trZ5AhSlUgWzmQEKERGRNzAwIaJZzWQyITklBWVlpUjPyJi1JxdaldInh+XMRhyaQxOhUCphtphhtpgB9AUobW39AxTlkSE8Zuj1DFCIiIgmgoEJEc16EeHhsPX0oKqyCikpycAsO6GwQg4teqUugwZhlwlNhuJIQBJmHhigVFb2C1CODOHR6/UMUIiIiEbBwISICEB8fDx6bDbs27cfcfFxUpdDsxyH5pC3jBigVFXB2t3NAIWIiGgUDEyIiI5IS01FYWEhGurrER4RIXU5M47DcnwLh+bQdBg2QGlvR1VVFbq7u/uG+ISFwWKxcA4UIiKa9RiYEBEdIQgCcnJysH37DiiVSoSEhkpd0ozhsBzfxS4Tmk6KIx0mYWFhAACn04m21laUl/cN4dFo1J5ljjWzeAl2IiKanRiYEBH1IwgCFi5cgK1bt0KpVEJvMEhdEs1i7DKhmaZQKAZ0oNhtNrS2tqKwcA8cDjt0Oh0sFgvCzGaouBw7EREFOAYmRESDyOVy5OblYevWrUhLTYVGq5W6pBnDYTm+py80AbtMSBIqtRrhERF9wxRFEdaeHrS2tGDfvn3o7e1FcHAwLBYLQkNDIZfzNUpERIGFgQkR0TBUSiVyFy7Ejh07kJGRAaVKJXVJ047Dcnwbh+aQ5AQBWq0WWq0WUdHRgCiis7MTjY2NqKyqggAgJCQEFosFwcEmyGSc/4SIiPwbAxMiohFotVrMnTsXhYWFyMzKmjXfnrLLxPdwaA75JEGA3mDwDF0UXS60t7djf10dSkpKIJfLEWY2w2KxwMAVeIiIyA8xMCEiGoXRaMScOXNQWlqKzIwMCDKZ1CVNK3aZ+C4OzSFfJ8hkCDaZEGwyAQB6e3vR1tqKqiNLGKtUKlgsFpjNFgQFzZ6hjkRE5L8YmBARjcFsNsNmt6OiogJpaWkAvyUlCXFoDvkLuVyO0LAwhB5Zgcdht6O1tRXFJcWw22zQarWeAEWtDvxhj0RE5H8YmBARjUNMdDRsPT3Yu3cvEhITpS5n2nFYjm/i0BzyZ0qVCpbwcFjCwwEAPdYetLa24MCBA3A6nTAYDH0r8ISFQaHgn6hERCQ9/jYiIhqn5ORk7NlThIMHDvRNeBigOCzHt3FoDgUKjVaDSG0UIqOiAFFEV1c3mltaUFNTA1EUYTKZYLFYEBISAlmAD4ckIiLfxMCEiGgCsrIysbOgAE2HDyPMbJa6nGnFLhPfxqE5FFAEATq9Djq9DjExMRBdLnR0dOLQoUMoKy+HXCZDaGgoLBYLjEYjJ5AlIqIZwcCEiGgCBEHAgvnzsXXrNiiVShiDg6UuaVqwy8S3cWgOBTpBJoMx2AhjsBEA4OrtRVtbG2r27kV3VxcUSiXMYWGwWCzQ6XQMUIiIaFowMCEimiCZTIa8vFxs2bIFycnJCNLppC6JZil2mdBsIZPLERIaipDQUACA0+lEW2srKioq0N1thVrNFXiIiMj7GJgQEU2CQqFAbl4etm/bhvT0dKjUaqlL8jor5H1zZXBYjk9yd5kwNKHZSKFQIMxs9gyNHG4FHrPZArPFDE0Afj4TEdHMYGBCRDRJGrUa8+fPx65du5CZlcVVHUYgiiIqq2vQ1NyCsNAQpCQlsn3eSzg0h6jP4BV4bD09aG1tReHu3XA4HNDr9TCbLQgzh0Gl5HuGiIjGh3/dExFNgV6vR1ZWFkpKS5GVmQkhwFZy8EaXSWV1Db746hs47HYoVSoAQGpykrdKJHBoDtFgao0GEZGRiIiMBEQRVqsVra2t2LevFr29vTAag2GxmBEaGsqwm4iIRsTfEEREUxQSEoKkxESUlZUhPT0dYPfEAE3NLXDY7UhLSUZ5ZRWamlsYmHgRh+YQjUEQoA0KgjYoqG9JeFFEV1cXmpqaUF1d7VnC2HxkCWN5gAXfREQ0eQxMiIi8IDIyEjabDVXV1UhOTpa6HK+byhLDYaEhUKpUKK+sglKlQlhoiJerIw7NIZoAQYBOr4dOr0cMANHlQmdnJ+oPHUJ5eTlkgoDQ0FCYLRYEG4MhkzEEJyKarRiYEBF5SUJCAnp6elC3fz9iYmOlLsdrprrEcEpSIgAMmMOEvK8vNAG7TIgmSJDJYDAaYTC6lzB2ob2jHfv37UNJZzHkcjnCjixhbDAYOAcTEdEswsCEiMiL5syZgx9//BGNDQ2eyQcDxWS7TARBQGpyEofhzBAOzSGaGplcBpPJBJPJBADodTrR1taG6upqdHd3Q6lUwmw2w2yxQBcUxACFiCiAMTAhIvIiQRAwd+48bN++DUqlEqaQwBh+MtUuE5oZnM+EyPvkCgVCw8IQGhYGAHA6HGhtbUV5WRms1h5oNGpYLBaYzWZotVqJqyUiIm9iYEJE5GUymYDc3Fxs2bIFSqUSOr1e6pK8ZipzmdDM4HwmRNNLoVTCbLHAbLEAAOw2G1pbW1FUVASbzYYgnQ7hFgvCwsxQq1USV0tERFPBwISIaBrI5XLk5eVh69atmDNnDtQajdQlTRm7TPwLu0yIZoZKrUZ4RATCIyIAAD1HljCuq6uD0+mE3mCAxWxGmNkMJZcwJiLyK/zUJiKaJiqVCgsXLsTOnTuRmZkJhTIwvvVnl4nv49AcIulotFpEarWIjIoCRBHd3d1obW1FbW0tent7YTKZkJ6eDhmXLyYKeEEKOXRK3/897BJ9v0ap8JOaiGgaBQUFIScnB8UlJXAEQMjAk2//wVCLyAcIAoJ0OkTHxCAjMxNZ2dmwWq3o6uqSujIiIhoHBiZERNMsODgYOTk5KCkphq2nR+pyvIJzZPgHq93BYVREPkQQBOh0OnR1d0tdChERjQMDEyKiGRBsNGLBggUoKytDt5//ocwuE//D0ITId6g1GnR3+ffvASKi2YKBCRHRDNHpdMjLy0NVZSU6OjqkLmdKrJCzy8RPuIfmMDQh8g0ajQbd3RySQ0TkDxiYEBHNII1Gg/xFi1BbW4vW1lapy6FZgqEJke/QqDXotlqlLoOIiMaBgQkR0QxTKZVYvGgRDh08iKbDh6UuZ9LYZeJfOAkskW+QyWVw9TK8JCLyBwxMiIgkIJfLkZ+fj5aWFtQfOiR1OTRLcBJYIt8gSl0AERGNi6SByTPPPIN58+bBaDTCaDRi2bJl+OSTTzzXv/POOzj55JNhNpshCAIKCgrGtd/W1lZcd911iIqKgkajQWZmJj7++ONpuhdERJMjk8mwcOFCWK092L9/v9TlTAq7TPwTQxMiaSnkcjgc7PoiIvJ1kgYmsbGx2LhxI7Zt24Zt27bh+OOPx1lnnYU9e/YAALq6urB8+XJs3Lhx3Pu02+1YuXIlampq8NZbb6G0tBTPPfccYmJiputuEBFNmiAImDs3BzJBhprqakD0z+8dGZr4D85nQiS9volfuVIOEZGvU0h58DPOOGPAv++77z4888wz2Lx5M7Kzs3HJJZcAAGpqasa9zxdeeAHNzc34/vvvoVT2/QGfkJDgtZqJiLxNEARkZKSjqqoK5RUVSE1NhSAIUpc1blbIefLtZ6x2B7QqJbTo5TLRRBJQq9Xo7u5GcHCw1KUQEdEofGYOk97eXrz++uvo6urCsmXLJr2f999/H8uWLcN1112HiIgI5OTk4P7770fvKJNr2Ww2tLe3D/ghIpppycnJiAgPR2lJCUSXS+pyJoxdJv6Fk8ASSUej0aCLHSZERD5P0g4TANi9ezeWLVuGnp4e6PV6vPvuu8jKypr0/qqqqvCf//wHF110ET7++GOUl5fjuuuug9PpxJ133jnsNg888ADuvvvuSR+TiMhbYmNjoVAqUVxcjPSMDMjl/vHtP7tM/FNfpwnYZeKDxvN+4vPmvzQaDVpb26Qug4iIxiB5h0l6ejoKCgqwefNmXHPNNbj00ktRVFQ06f25XC6Eh4fjr3/9K/Ly8rB69WrcdttteOaZZ0bcZv369Whra/P87Nu3b9LHJyKaqsiICKSlzUFxURGcfjYpILtM/BPDLt/ifj6sdseIP+7buX/Iv6jValitVqnLICKiMUjeYaJSqZCamgoAyM/Px9atW/H444/jf//3fye1v6ioKCiVygHfymZmZuLQoUOw2+1QqVRDtlGr1VCr1ZO7A0RE0yAsLBTZ2dkoLCzEnPR0v/iMYpeJf+J8Jr5Fi95xDZfqfxv38wew68RfCDIZRNH/hl4SEc02kneYDCaKImw226S3X758OSoqKuDqN/6/rKwMUVFRw4YlRES+Kjg4GPMXLEBZaSmsfjTWnV0m/ocr5/iG8YYlgw3uOiH/IIoiRD9dGY2IaLaQNDC59dZb8e2336Kmpga7d+/Gbbfdhq+++goXXXQRAKC5uRkFBQWeITqlpaUoKCjAoUOHPPtYs2YN1q9f7/n3Nddcg6amJtxwww0oKyvDRx99hPvvvx/XXXfdzN45IiIv0Ot0yMvLQ2VlJTo7OqQuZ0z8dtt/8YRbWt543N3BCYfp+AelUgm73S51GURENApJh+TU19fjkksuwcGDBxEcHIx58+bh008/xcqVKwH0rXhz2WWXeW6/evVqAMBdd92FDRs2AABqa2shk/2U+8TFxeHzzz/HjTfeiHnz5iEmJgY33HADbrnllpm7Y0REXqTRaJCfn4/t27cjNjYWwSaT1CWNSatSjvlNeW9vL2w2GxwOBwSZDHK5HHL3f+VyCDIZ/Gdx5cDgHp5D0vDWykX9h1kBDDJ9lUajQbfV6hdDLomIZitBZC/gEO3t7QgODkbbwVoYjUapyyEiAgA4nU7s2LEDlvBwhIWFSV3OqAYPLWhpacHhw4d/+jZVEKCQy6HVaqFSq+Hq7YWztxe9Tqfnv/2HVooAIIoQZDJERUbCFBLCMGUaaVVKnmTPoP6TvHp930cCMD6fvqf+UD2CgrSIiYmRuhSaaUHBUlfgEzznXG1tAXfO5b5vBRdeDIMfTAvRYbdjwT9eCcjnYqokn/SViIjGR6FQID8/Hzt37oTT4UREZITUJY1Kq1Kio9uK6upqKJVKZGZmQqPRQBAmH3XY7XZUVFSgrq4OsbGxMPlBt42/4iSwM2s6whL3fjmpr2/SaNTo9qP5qYiIZiOfm/SViIhGJpPJkJubC6u1G3X790tdzoiskMPhsKOoqAgJCQmYO3cutFrtlMISoG9ltaysLOTl5aGjowOFe/agtbXVO0XPIK1K6dNDXzifSWDh8+mbNBoNuhiYEBH5NAYmRER+RhAEzJ07FwCwt6YG8LWRlaKIiooKOJxOLF26dFqGD7mDk9zchWhvb8eePXvQ1tbm9eNMlTsYGfxjhbxvGeYRrveFMIUn2YGFE8L6HrVaDVtPj9RlEBHRKBiYEBH5IUEQkJmZiaCgIFRUVPjU0pQNDQ0w6A0IDo+GHNNbl1qlRnZ2NhYuXIi2tra+4KS9fVqPOZbBoYc7HOn/4zbcde7rfSE8YWgy/Sa7lPBk8Tn1IYIAlw99dhMR0VAMTIiI/FhKSgosFgvKSksh9pskVSpOhwMNDQ1ISUme0eOq1T8FJ60tLSgqKkL7DAcnI4UkkzFSeCKFmTyZp5nB0MS3+FLgTUREAzEwISLyc3FxcYiLi0NxcTF6e6U9AaqpqUFGRsaA5d5nklqtRk5ODuYvWIDmGQpOhgtKvMkXghP3UA4KHAxNfINKpUKPzSZ1GURENAIGJkREASAyMhKpaWkoLiqG0yFNR0B7WztkMhlCQ0M9lwlaPdDrnPFaNGo15ubkYP78+Whubu4LTjo6vH6c6QxKBvOF4IQn14GFoYn0NGoNrJz4lYjIZzEwISIKEOawMGRnZ6G4uBj2Gf7GUnS5sLd2L7Kysmb0uGPRaDSYO3duX3DS1ISioiJ0eCE4GTx560ySKjjhyXVg4vMqLbVGja6uLqnLICKiETAwISIKIMHBwZg/fz5KS0thtVpn7LgHDhxAXGwsVCrVjB1zItzBybz583G4qQnFxcWTDk76d5VIaXBwMiPH5Ml1QOLzKh2tRoNudpgQEfksBiZERAFGr9cjNy8PlRUV6OrsnPbj2Xp60NbWhri4uGk/1lRpNRrMmzsXc+fOxeHDh1FcXIzOCTxGvhKW9CdVaEKBhaGJNDQMTIiIfBoDEyKiAKTVaJCfn4+amhq0tbVN67Gqq6uRlZ0NQRCm9TjepNVqMW/ePMydOxcHDx5EdU0NXKOsMiTlEJzxcNc1k0N0eGIdeBiazDylUgkbJ30lIvJZDEyIiAKUSqXCokWLcPDAATQ1NU3LMZqbmqDX62E0GKZl/9NNq9UiNzcXFrMZhXv2DLuiji92lYxkprpNeGIduPjczjBBABcVJiLyXQxMiIgCmEKhQF5+PtpaW7Gvdh8geu9Pc1dvL+rq6pCenj7ibUTr9A8J8oaoqCgsys9HQ0MDqqqrPd0m/hSWuM10aEJTJ8WKR6NhaDKzBAAuF2MTIiJfxMCEiCjAyWUyzJ8/H1qtBqWlpXD1euckqLa2FimpqZDLxwgT5AqvHG+6qVQqLFy4EBHh4SjcsweKI78h/SkscZvJ0IQn1VPjq68vhiYzR61Wo6dn5ibpJiKi8WNgQkQ0CwiCgOTkZCQmJqKoqAi2np4p7a+7qwt2ux2REREj3ka0dvpNWNJfZGQkli3Oh91mx57KGq8FTDNtJieD5Ul1YGIX0cxQc+JXIiKfxcCEiGgWsVgsmD9/PsrLy9HW2jq5nYgiqqurkZ2dPfzV1k6/GYozrF4nZIIMpsgYREVFYc+ePdM+ce50mYnQhCfVgY1dRNNPo1YzMCEi8lEMTIiIZhmdTofFixejvr4BBw8cmPD29fX1sISHQ6vVDrnOE5TIFX7ZXeImaPUAgIiICCxevBhNhw+jqrISvX7YbdJ/BZ1pOwZPqgMen9/po9Fo0NXVJXUZREQ0DAYmRESzkEKhQF5eLgCgorwc4ihL6vbndDjQ2NiI5KRkz2XujpIBYYm/6nV6whI3pVKJBQsWIDo6GkV79qB1sp05Epvu0ATgSXWg4nwm00vDITlERD6LgQkR0SwlCALS09MRFRWNoqIi2O32Mbeprq5GRmYmBFvX0JDEz7tK0Osc9erw8HAsXrIELc3NqKyo8Ntuk+kKTTg0Z2q8+byIooiKqmr8sG0HKqqqIXphdSyGJtNHoVTC4eD7h4jIFzEwISKa5aKiIpGdk4PSkhJ0dnSMeDuFswepsVEI0Rw5sQuEkMTtSFgyuLtkMKVCgfnz5yM2Nrav26SlZSaq86rp7jThCfXEeXulnMrqGnzx1Tf4Ydt2fPHVN6isrvHKfhmKTR8uKkxE/qajowO/+93vkJCQAK1Wi6OOOgpbt24ddZuvv/4aeXl50Gg0SE5OxrPPPjtD1U4eAxMiIoLRYMCiRYuwf/9+NDQ0QIveAT8aOLFj125o9IbACUkGGSss6c9isWDJkiVobW1FRUUFep2jd6f4mukKTXhC7RuamlvgsNuRlpIMh92OpmbvBXucr2Z6yATBL7vWiGj2uvLKK/HFF1/g5Zdfxu7du3HSSSfhxBNPRF1d3bC3r66uxs9//nP87Gc/w86dO3Hrrbfit7/9Ld5+++0ZrnxiGJgQEc1i/ecfUfbakZ+TiXBDEPaUlqHbZofV7oDV7kB5VQ3i4+OhUk7/ErUzboyhOCNRKBSYN28e4uLiUFRUhBYvnpTOhOnsNOEJtbTCQkOgVKlQXlkFpUqFsNAQrx+Dz7F3qdVqWK1WqcsgIhoXq9WKt99+G3/5y19wzDHHIDU1FRs2bEBSUhKeeeaZYbd59tlnER8fj8ceewyZmZm48sorcfnll+Ohhx6a4eonJvC+IiQiomGNuNRvv24RAYDBFILQzi4UFxcjbc4c9Dqd6GhvR1Zm5swUKoGJdJcMZjGbEWIyobi4GIcPNyI5ORlyhX/8eu0LTbzbGWK1O6Z9clkaXUpSIoC+TpOw0BDPv73F/Rxr0ev14USzVd/Er1bo9ZP/LCIimqr29vYB/1ar1VCr1UNu53Q60dvbC41GM+ByrVaL7777bth9b9q0CSeddNKAy04++WQ8//zzcDgcUProl3L+8RcdERFN2LAByTiH0sTFxsKg12PPnj0QZDLMmzvXy9UFFoVCgblz56KpqQlFRUWIjY1FSGio1GWNy3SEJgB4Mj1B3nweBEFAanISUpOTvFDZ8BiMeZdao0F3dxcAi9SlEJEXabVyaFW+/7vQKe+rMS4ubsDld911FzZs2DDk9gaDAcuWLcM999yDzMxMRERE4LXXXsMPP/yAtLS0YY9x6NAhREREDLgsIiICTqcThw8fRlRUlHfujJcxMCEiChBTCUiGYzKZkJefj5bm5sD91nOSw3FGEhYWhqVLl6KouBiHDx9GUlISFD76jUl/3g5NeDI9O/Q9z96ftHY20qg1aG5ulroMIprl9u3bB6PR6Pn3cN0lbi+//DIuv/xyxMTEQC6XIzc3FxdeeCF27Ngx4jaCIAz4t3sVt8GX+xLOYUJE5OdGXN7XCxOzatRqn038vWUqw3GGI5fLMTcnB0lJSSguLkZzU5NX9z9dpmNOE85zMTvweZ46jVaDbmu31GUQ0SxnNBoH/IwWmKSkpODrr79GZ2cn9u3bhy1btsDhcCApafjuxsjISBw6dGjAZQ0NDVAoFAgLC/Pq/fAmBiZERH6o/2StAbW8bwAJDQ3F0qVL0dnVhbLSMjgdvr+CjDdDE66YMzn+1pnjfp4ZmkyNXC73u9W2iIgAQKfTISoqCi0tLfjss89w1llnDXu7ZcuW4Ysvvhhw2eeff478/Hyfnb8EYGBCRORXhu0mIZ8ll8uRk52N5OQkFJeUoMkPuk2mc/UcGp2/Dm1hOOYdotQFEBFNwGeffYZPP/0U1dXV+OKLL3DcccchPT0dl112GQBg/fr1WLNmjef2V199Nfbu3YubbroJxcXFeOGFF/D888/jD3/4g1R3YVwYmBAR+QkGJdNArhh59SAvCg0NxdIlS2Dt7kZZaanPd5tMJjQRRREVVdX4YdsOVFRVe8Yls/Ng9uBzPTVymQxOdpkQkZ9oa2vDddddh4yMDKxZswZHH300Pv/8c0+3yMGDB1FbW+u5fVJSEj7++GN89dVXWLBgAe655x488cQT+OUvfynVXRgX/sVNROTjBpzQMyjxW3K5HFlZWWhpaUFxcTGioqIQZjZLXdaIJjoRbGV1Db746hs47HYoVSoAQGpyErtVJkGrUvpd1waXGp46tUaDbqsVRoNB6lKIiMZ03nnn4bzzzhvx+hdffHHIZccee+yok8L6InaYEBH5MHaVBJ6QkBAsWboU1p4elJaUwOHDJ8buTpPxhB5NzS1w2O1IS0mGw25HU3PLDFQYePw5bPC3kMfXaNRqdHd1SV0GERH1w8CEiMhHDQhLaFrNxLCc/uQyGbIyM5GWlobS0hIcbmyc0eNPhPsEfqzQJCw0BEqVCuWVVVCqVAgLDZmJ8sjHWO0ODs2ZJI1Gg+5urpRDRORL+Fc4EZEPYlgyg+QKoFeaeQNMJhOWLl2K0rIylJSUICU52TOcxZdYIYcWvaMOFUlJSgTQ12kSFhri+TdNjj8Oy+mPQ3MmTqPRoNGHw1MiotmIf4kTEfkYhiXSEK2dELT6GT+uTCZDZkYG2trasGfPHkRERMBisQCCMOO1jGas0EQQBKQmJyE1OUmC6gKL+7H2V+75TGhi2GFCROR7OCSHiMgXMSyZWT7weAcHB2Pp0qVwOp0oKSmB3W6XuqQh+g/P4QkxjYZDcyZOkMngcrmkLoOIiPphYEJE5ENEa6dPnLzPSjO0xPBoZDIZ0tPTkZ6ejrLSMjTU1wNHluf1FVbIxz2vCU1NIDy+DE0mRvSx9zsR0WzHwISIyEdIfbJOfXzheejrNlkCl8uF4uIS2G02qUsaYjzdJoFwwi+VQJj/w5/nYJGKQqGA3cHHjYjIVzAwISLyJewukZYPPf4ymQxz5sxBZmYGysvL/aLbpH9A4v7/QDjxp6lhl8n4aTQaWDmPCRGRz2BgQkTkA3yhq4GO8IGhOf0ZjUYsWbIUoiiiuLgYXZ2+U5ubOzixQj4gOGFYMnX+3qXDLpOJUavV6GJgQkTkM3znqzQiotnOh7obSLpVc4YjkwlIS0tDV1cXKioq0FNbi6joaJiCg31yNR3yDn9fLcetb9UcvjbGQ6PRoLurS+oyiIjoCP51TkQkMV/qZhi37raxbxMUPP11TBe5Auh1+lRoAgA6nQ7z58+HzWZDZVUV9u/bj8jICISZzRB8LDgh7xlpKWd/o0UvQ5Mx6A0GVFZUSF0GEREdwcCEiMgX+Fp3yViBiEI99X34eqDio6EJ0Ne2n5WZCafTiZq9e1G4ezfMFgsiwiMgk3O0bSAJrC4T/x5eNBMUCgWcTidcLhEyGUNQIiKp+dhf6EREJJnBAcd4QpHRjLa90zZ8oOJrIcqR0MRXKRQKpKakIDkpCfvr6lBUtAfBwSZERUVCoeTJKfkedpmMTW8woLW1BaGhoVKXQkQ06zEwISKazfqHFlMNSCZiuGMNDlF8JTw5Mgmsr3WZ9CeTyRAfF4e42FjU19ejtKwMQVotomNioFbP4PNK0yYQhuWwy2R8QkwmNDQ0MjAhIvIBDEyIiCQkWjulGY4jVVAymv51+Fp44gehCQAIgoDIyEhERkaiubkZlZWVkMlkiImJQZBOJ3V5NEmBMiwH4ASw42EwGLB//36pyyAiIjAwISKaXdwhhK+EJCPx0fDEH0ITt9DQUISGhqKjoxOVlRWw2+2Ijo6GMdhHOndowgKhy8SNQ3NGJshkEAQZHA4HlBxaR0QkKc4MR0Q0G3S3+U9YMphC/dMPMPC+zKQjnUD+tqqRwaDHggULMHfuXLS1tWFPYSGampoAUZS6NJqAQAoXAiX0mU4mU3Df+5SIiCTFwISIKND1D0r8LSwZTOrgxE9DEwDQarXIzs5Gfn4+HA4Hdu/ejfpDhyC6XFKXRrNUoAwzmg6mkBA0NDRIXQYR0azHwISIKJD5a1fJWKQMTvw4NAEApVKJOWlpWLp0KZQqFQoLC1G3fz96nb67GhD1sUIeMJOmsstkdFqNBl1dXVKXQUQ06zEwISIKVIEalvQnVXDi56EJAMjlciQmJOCoo45CcHAwSkpKUFNdA4fdLnVpNIuwy2QEggCVWo3ubqvUlRARzWoMTIiIJDKtK+TMhrCkPymCkwAITYC+lXWio6OxdOlSREdHobKyEhXl5eix8kTNV7HLZHYwmUxobOSwHCIiKXGVHCKiQHMkLOhxiiguLIDNZoPZHIaoyCjoAn1pWXdo0n9lnelcVUeuAHqdfrV6zkgEQYDZbIbZbEZbWxsqKyvR29uL6JgYGAwGqcujIwJpiWE3rpgzvBCTCdXV1UhISJC6FCKiWYuBCRFRIOlugygCZdV70dLSgoSEBAQFBaG1tRVlZWXo6elBsMmEqKgomEwmCF4+vNg28rehQnC4l482ipkMTvp1mvh7aOIWHByM3NxcdHd3o7KyEvtqaxEVFYWQkBBA8ParhiYjUJYYttodAdMx420KpRJOzi1ERCQpBiZERAGkp8eG7T8WIjo6GllZWZ5AJDQkBKEhIRABdHZ0YP++fSguLoZOp0NMdDTCzOZxhyejhSJQBU1qu2kLUwYHJ9PcbeIenhMowUlQUBDmzp0Lu92O6upqFBYWIjwiAhazGYKMo3qlwi6T2UPk8t9ERJJiYEJEFAB6XS50NRwAAOTkZEMmDH8yKwAwGAyeIRbdVisO1dejrKwMYWYzEhMToVaphmw3JOwYJRgZ0QTCFK8HKAr1zHWbBMgQnf5UKhXS09PhdDpRW1uLwsJChIWFISIiAnIF/5SQCrtMiIiIphf/yiEi8hF1dXVoamqC1WqFwWBAVlYWAGD//v3YX1cH0eVCZmYmTCbTgO06Ozvx4+7dWJCWhF5BPqFhNkFaLRLi4yECaG1tQcHOndAGBSEtNRVarXZgkDGZkGS8Bu17WgKUmeo2CdDQBAAUCgWSk5ORmJiEgwcPoLS0FGq1GtHR0dAGTePrg4Zgl8nsIYoiBA6FIyKSBAMTIiIfUVtbi5SUFGg0GpSUlsJqtUKr1aKurg6ZGRno7e1FcXExli5dCrvdjrKyMnR3d8MlisjLTINcJoPVMbkTKAFAiCkEIaYQdHR0oKdhP2yCAJlWB4Neggk/+wco9u4BAcqUw5P+q+kA0xOcBHBoAgAymYCYmBjExMSgra0N1dXV6LHZEG4JR0iIiV0nM4hdJoFNqVTCbrdDrZ4lK54REfkY/kVDRCSxuro61NbWQqPRQK3RQBD6TkYLdu1CVlYWRFGETCbDvn37oFKrUVlVhcaGBsTHxyMxMREAIBcdkw5L+tPYO6FRC4Baj6aObuwvK4PoEpGamjqks2XGjBCeeCU4mc5ukwAPTdyCg4OxYMEC2O121NXVobyiAr1OJwxGI0JMJuj1es53Mk3YZRL4GJgQEUmLgQkRkYSam5uxv64O2dnZA1qujQYDUlJSUFpS4gkqenp6MH/+fGzfvn3I7b1BY++bsNR65PwrKCgIc9LmwGbrQU1NDex2O+akz4Ep2OTV405Iv/DEK10n091tMktCE6BvnpOkpCQkJSXB5XKhpaUFDQ0N2L9/P1wuF1QqFXQ6PfR6HXQ6HbtQvIhdJoFLqVTCZrNxaW8iIonwrxUiIgkVFRWNGH5oNRpkZmZ6/i0IArZv347Ozk7Y7HZojnzjqPVCd8ngsKQ/tVqDlJSUvpVSqqoACMjIyIBWq53SMafMHZ54o+tkOrtNZlFo4iaTyRAWFoawsDAAfXMw2Gw2tLW1oa2tDQcPHYLT4YBcLodOp4fBoIder4dCyZPliWKXSWBTKJSw2e1Sl0FENGsxMCEiklB2Rjpk4xyuMGfOHADenwBwtLCkP5VKhdTUNHR1deHHH3+E0WhAWtocKKTuFPBWcDKd3Sbyvsco0JYdHi9BEKDRaKDRaBAREeG53OF0or2tDS2traiurobd4YBcJoNe3xeg6A0G6V9ffoJdJoFJpVLC1mOTugwiolmLf4UQEUlE0Ooha2tDQUEBQkNDERsbO66TQynCkv50Oh2ysrLQ0tKMLVt+QHR0NBISEkevq+nAwH+HRU+i2jF4Mzhht8mMUCoUAzpRAMDhcKCtrQ2tra2oqqyEw+GAQqGATqeD3mCAQa/ncJ5B2GUSuJRKJazWdqnLICKatfgXBxGRxJKSk9HR3o5du3YhLy9vQttqxcl/ozyZsKS/kJBQmEwhqK8/hE2bNyEvIWrkiQmDBo2/HxygDDaVQMUbwYmXu03cQ1JUKhVkDE1GpVQqYTabYTabPZfZHQ60tbaitbUNDfX1nhClfyeKXM6Ta3aZBB73HCZERCQNBiZENGu4h7LYHQ5UlJcjNjYWRqNRsnp2796N+PAwmIKDEWIywWazoaioCKmpqVCpVOPez2TmL5lqWOIWZG1FklGDREMMrD09KCirREZGBoKDxwgYBgco/XV3eKcjxVvByRS6TewOB0qKi9HZ1QW1SgWn0wmXKEIAkJmeBmV3F6yiHAaDgatgjEKlVMJiscBisXgus9vtnk6UgwcPwuVywWQyISwsDGqNRsJqpcEuk8CkUqk4hwkRkYQYmBDRrPHNN99ArVaj1+WCVqNBR0fHjAUmdrsdVqvVEySIooiOjg4EJcZ5vhFOSUlBfX09duzcidyFCycUmkzGVMISbXfLT/uBAhAAQatESkoKKsorIJfLkZGRAY12Eieug8OUwQHKRMOTQcHJlEITYNzBicPhwPZt2xAfH4+kpKQB14kA7DYbWttbEWoyorulETuq90EQBOh0OhiMRhgNBhgMBig5EeqwVCrVgBDF6XTi8OHDqKurQ3d3N/R6A8LCQvtWF/HyilK+jF0mgcXhdHIeHyIiCfETmIhmDbVGg4z0vklW29s70NLaipiYGK8eo62tDY2NjTAag9HV1YnOzk6kpKSgtrYWjY2NiIqORq/TicNNTQgNCRmwrUwmQ1RUFERRRG1tLVJTU71am5u7u2Qy3EGJdYRfH2qVGnPmzEFnZycKdhUgxBSC1NRUyBVT+Ja4f4AylfDkSHAyqW4T9xCdcXabOJ1ObNu2DQkJCcMuByoAUKvVnq4SvUaL/JxMWEUZenp60NXVhYMHD6KishK9vb2QCTLo9ToYjUYYjUYYDIZxTxY8WygUCkRGRiIyMhKiKKK1rQ2HDh5ETU0NzGYzIiIiIAvwYTuB2GUy23W0tyNk0O8KIvIfWo0CQWrfP+V2ylxSl+CzfP/ZIyLyErVK1XfyKZfDGGzEgQN1aG9v92qXic1mQ1NTU9+3gnI5wsLCULhnD3qsVixYsAD19fXQarWYm5Nz5FvvgSc3oiiiuaUF2mkeUjCZ7pKxwpL+9Ho9srOy0dTchB9++AGxsbGIi4+b+oS1I4UnEw1OJjtMZ5zdJnV1dbBYLMOGJcNxf5uuFVyAVguNVouwfteLLhesViu6urpQW1uLzs5OqNVqREVFITw8nPN3DCIIAkJMJoSYTHC5RNQdqENRURFMJhOioqICftLYwOoywaweltPZ2Ym4uHipyyAimrUC+y8GIiL0DYfZunUr5HI5xH6XJyUno6i4GEuXLJnwPvfu3QuzxQJd0JGuhSNdIXV1dUhKSoJO/9NknhkZGRBFERAERERGjr5jQYAATFv3wGS6SyYSlAwWFhqG0JBQHDx0CJs2bUJ2dvbY85uMV//wZKJdJ1MZpjOObhOHwzHhOUk8ocmREK3/SaIgkyFIp0OQTue5zGazoelwE/bu3Qu5XI7IyEhEREZCxSE8A8hkAuJiYxEbE4NDh+pRUlICvV6P6OhoKKd52JsU2GUSWLq6umA0ji94JSIi72M/LxEFvKKiIiQkJCArO3vAvCBqtRqiKPaFGRNQV1eHw4cPo6CgAHZH37e4Bw4eRHNzC7KzsweEJW7j7awQAGRmZuLw4cMTrmu8JtJdMpWwxE0QBERHRSEjPQOlpaWT3s+oggw/BShNB8ZehcdNFQSogiC2NXg6TsZt8Eo6/Th7eyfV9WG1OzydAWOd9KrVakTHRCMrOxspKSno6enBzh07sOvHH+F0Oid87EAnCAKioiKxdOlSREZFoaKiAlWVlbD19Ehd2rQIpPk/Zm0AJIoQMX0BOhERjY0dJkQU0BobGyGKIowjdDUoFQo4HI5hJ1h1r6ozmN1uR1hYGJQqFbb88APkcjmUShUSExMgTPAPWyvkfS3n/drnZTIZQkJDcfDgQURHT2F53SnyRljipulogFoUkWpUQ6zfO65thIiEiR/IHZpMdLjOZIfpjLD8sEqphH0KK1v07zYZz3AEhVKJiCMdJq0tLfjhhx8wZ86cAavKUB9BEGAxm2Exm9HS0oKKykrIBAGxsbHQHukY83eB1GUymyd/tfb0QN+vq4yIiGYeAxMiCli9vb0oKytDVlbWiLdRHjmx7R+YWHt6sG3rNhiDjZg/b57ncqfTicI9e9DZ0YGk5GQYDAZkZWX1rWDg5VU4YqKjUVRc7NXARGPvnPDcJZMNSzQdA7s1rHIN9u7di/DwcEA/jgkMO1uGBCsTClAmE5xMdZhOvyE6UVFR2LNnD8LCwsbedgSjDdEZjSkkBAaDAaVlZVAqlTCZTJOuIdCFhIRgUX4+2js6UFFeDmdvL2JjYqAf59wzvi5Q5jKZrTra22HihK9ERJJiYEJEAau7uxsul9g3BnykDhOlEkVFRejp6UFOTg5CQ0PR1dkJtVoFteqnOSg6u7qwa9cuJMTHIzk52XO5Yprmi9BqtZDJZNi3bx96e3uRmJg4LccZ8fjdLZMKS/oHJVb5TxPXii4XOjo6kJOTM74dDROqTCpAmWxwMtlukyOhiRZAUFAQamtrERcfj8nGaZ7hORPoNgEAuUKB+Lg41NXVMTAZB6PBgNzcXHR3d6OiogK1+/YhJjoawX782AVel8nsm/y1s7MTkWPNe0VERNOKgyKJyG+NNf+IwWDAvHlz0dTcPOJtIiMjoVKpYAkPR2hoKFpbWz0hRVCQ1nO7kuISzElLGzF4mQ7JyclwOBxobm5GV1fXjB3XPRRnIjQdDdB0NMAq13h++hNksqmPw9eH/PSDvgDF/TOmic5xcmRuEwATm9tEofYM08lKjIFarUZVZeWU56MZ77wm/en1erS2tQ04tiiKaG1txeGmJjQ2NqK+vh4HDx5E3YEDaGpqmtIwokAQFBSEefPmYcGCBejo6MCewkI0NzUB0zSf0EyYrcNZAoHVaoWOQ3KIiCTFDhMi8ksNDQ0oKipGSkoy4uLihlzvdDohiiJsNhvUo6yEoVAqodPpoFarYbPZsWfPHiQkJKKtrQ12ux3l5eVobW2F0+mEepqX+h0s2GhEsNGIpqYmVNfUICc7e8aOPd7ukpE6SoajVqtRWFiIzIyMqS/r2r8Dpd/wnTG7TgZ3nExzt0lqtAW1h1UoKytDWlralEKjn7pNjvx7rG/bBcETmoQc6ZQor6hAZ0cHtFotBEGAIAhHahLQ1tqKqupqOB0OyGQy6HQ6hIWFISwsbNg5fgKZRq1GVlYW7A4HqqurUVhYiIiICJgtlqkvjT2DAqnLBMCEuqz8nehyQSaT+dXrjYgoEDEwISK/YrVasWdPEZQqJWJjYwZ0XvT29nomat28eTPUGg2cDgfi4+NH3afZYsGPu3bhwIEDSE5Ohk6vR2+vE52dXTAYDUhNTZ36Cf5o92mYiV/7Cw0NRVVV1YiT0I7HeOcvmUh3iTssGSsocUtJSUHLkQlJU1JSxl5iebzc4cmgeU9GDU/6d5sAowcnk53b5EinSbw5GOEGDXbs2YPExEQYpjg/xkQmhDWbzThQV+cJTFqam5GZmTnmnDuiywWr1Yq29nbs378fTqcTeoMBiQkJU67fn6iUSqTPmQNncjJqa2tRuHs3LBYLIiIiJjzBs5QCYS6T2Tb5a2dnJwxGo9RlEBHNegxMiMgviKKIiopKNDUdRmJiIoJ0OlRVVqK724qtW7dCqVSiq7sbyiPBRnR0NMzjXCFELpdjYW7ugMtCQkMREhrq9fsxGYIgICQ0FHUHDiA2Jmbajzee7pKJhiVuISEhCDYGo2ZvDeobGpCTnQ2ZXA4cqO67QXTShOv1mEzXSZBh/PObTKHbRANgybxM7K6sxcGDB5GQkAC1Wj3mpiMZb2hi0OtRU1PjCdsUSiWcTueYc+8IMhmCdDoE6XSIiooCxL65gMrKy+F0OJGYmIDw8PBZ8+23QqFAcnIyEhMTsX//fhTu2YPQkBBERkVNavnomRRoXSazgiiitrYW8+cvkLoSIqJZj4EJEfk8URSxZ88eyBWKvhVvjpykJaekAOjrLLHbbNBqtV5frUZqoiiirq4Ozc3NyMrMnNZjjbe7ZLJhibblkOf/s0OC4HA40Fq8C0ajoW+loWDLT8HJSMYbqAzTdTJicDKRYTpT6DaRO21YkJYIh8OJHSUV0Ol0iI2NnfQJ97iG6BwZltPW1gaTyYSw0FA0NjYiaqKrLwkCdHo90tLS4LA7cOjQQVRVVWHuvHmzatlTmUyG+Ph4xMXF4cDBgygpLobBYEB0dPS0TQDtLYHQZQLMjmE5dXV1iIqKGjCPFhERSYOBCRH5vKqqKshkMsSM0F0hl8v/P3v/HSTZd5d3wJ/bOYfpyTnnzVmUAAFCr5EFCNu8LkQUcgESRhTIGIHLGEyQ8Uu0bAtjgQsDEq5CyBgTjIRA+um3eXd2d/JODj05dE/ndN8/erqnu6fj5Jk9n6pbu9P33NNnUk+f5z7f54veYDjhVZ0Mg0NDKBQKbt68ieIExKBC7pKDiCWpQolfY9o7oQFZFeLJ69fU1tTQaCUumuQjU1ApJKAcRDg5LrfJbomOGrhzqZtlt5+hoSGqqqoO5dYo5DYpLy9ncdGJzWajqamJh48eYbXZMBzwd0atUdPQ2EgwEODVy5fU1dXR0NDwxrhNIO76qqutpbamhtW1NcbHx9Hr9dTV15/JvJeTdpnIsszk9Awbm1s4yuy0tTQfyc/Hm1CW4/f5cLt36O7uPu2lCAQCgQDRJUcgEJwD1tfXqa+vP+1lHDvZNgLVVVWEgsETEUuKpVixRL+1jH5rGb/GlDwy0Wg19Pb24vF4efbsGdFogU2dtWLvgLiAkjjykaW7Tk6K7aZzmE46QLVFzzuu9iNJEkNDQ4fqhJTaRSdzY2w2mdh2bSfLcq5eucLkxAThQ7oNtDodvX19uN1uXr58eehOQOcRSZKoqqzk7t271Nc3MDU5yeTEBMFA4LSXlpWTEhsmp2f427//Mg+fPOVv//7LTE7PnMjznntkOe7cunTpjRIgBQKB4CwjBBOBQHAmkWUZt9vNzMwMkWj0wpXaZJLLYl5RUYHFYmF0dJS5uTlcLtexPH8x5TipHXHyzrUrlABZRZJMJEmiobGBiooKHj9+TDhc5Eb+IOJJhnCSk8w2xPlIEU2KFk522w9LErTVlHPt2jXm5+eZm58ndkDhwR8KZ28/nOiWs70NgE6no7+/n/HxMeRY7EDPtTe1RGNTE0ajMS54HXK+84zDUcbt27dpbW1ldnaO8bFxfD7faS8ryUmWsWxsbhEOhehoayUcCrGxWXqr8lz4Q+ELm8kiSnEEAoHg7CEEE4FAcKZYWV3l4cOHPHjwgOnpaWRZpucNtya3traiUqnY3NxkY3PzyOaVZZmJmTkeDrxkfG4Bn1x4Q1XIXZIqlBQjlqRis9lobGzkyePHpd+hzyWe5GJXODkrbhNdLMitW7ewWiwMDQ7iOSK3SYKa6mqmpve+HlarlZbWVl6/fg1H4AyprKrC4XDw9MkTIpHIoec7z1itVm7evEFXVxdLTiejo6N4dnZOe1lJTsJl4iizo9ZoeD05hVqjwVFmL3zRG47f52NnZ4empgKt0QUCgUBwoogME4FAcGaYm5tjeWWFrq6ueOcUAQCxWIxgKEQwGKSyssiQ0SKYnJ3nC199QCgUwkiEr0NFe2P20ifdzmpJYslBqQy6cFRa8I69RGmxoFIpoaGjtEkSoolrrXD3HZO9cL5JqaGwB2w/jM9FfZmZ8oobvHr1CovFQm1NTXFzZJAZCIteTzQaxefzJbNLqquq8Hm9zM3PF2y9XQxlDgdKpZLHjx9z48aNM5nlcZKYzSauXbuGz+djYmKC+fl56urqsFitp7amk8oyaWtpBkjLMBHkRt4txbl69aooxREIBIIzhnCYCASCM8HExAQbGxt0v+FiSba7v5FIhGAgwJ07d460I8nG9jahUIiOliZC4Qgb2wcv9zmIWKJfX9h3+PVWQqYyZHsljyZmcSn1MP86+1GIYh0nxeablOg2KalEB/bcJtEAN2/cIBaNMjU1dahskFS3SV1dHZOTk2nnW1tbkWMx1lZLWGcerLvBso+fPCFwRnM8ThqDwcDly5e5cuUKLpeLocFBtjY3j8TZc1CO22UiSRLtrS3cuXmd9taWIxcBLlpZjnO3FEevF6U4AoFAcNYQgolAIDhVEi2DA4EAbW1tFz6rJB+5MgZUajWxY8iGcNhsaDQaXk/PolGrcNiy3/kulF1SiliSKY5kHgk0Gg09PT0MDw+zHlNBWc3+o1gBJbVcpwThJCsHzDYpmkS2id9NT1Mtdrud0dHRwmG4eUiIJpVmA3XlNtxud9r5vr5+tl0ulpwFPp8iMZnNtLe18fTp00OVFl00dDodfX193LhxA78/wODgIBvr6ycunFz0lrzniUg4zMTEBKFwWJTiCAQCwRlFlOQIBIJTIxaL8eLFCwwGAzW1ecoc3nCUCgW5tlR+SY1eDf5w6RvqtqYGIO40qdVJ1Ddkb9sMhbNLCokl+vWFvbH64koSVCoVvT29jI2PEQ6HqanJ+BkpSylX2VxKF01ylfGkOk4ge6lOsWU6iRIdyF2mkyGalFSmEwnSWG7FYDAwMjJCd3c3KtXB/mwnRBOjwYhnZ5uQTpcsmVEoJK5fu8b4+DgTr1/T1taGpDjc/RS9wUBnZycvBga4dPkyFrP5UPNdJDQaDd3dXUQiEaanZ3j16hVVVVVUVFQc+uteCnqNOvlzIThhZJnl5WXW19fp7u6mrKzstFckEAgEghwIh4lAIDg1pqamMJlMQiwpBlk+8ratkiTR3tzI13c20NDYfCDbfMJdknfMrliS6SIpBoVSQXd3N06nk9nZmdwDU50nUJzrBHI7Ts6K2wQoN6i52dvJyMhI8R2EchCMRDEYDHg2VojF9n6eJEmiq6uL6poahoeHCYdCh3oeiLcd7u7u5tXLl7gyXC2CuCDY0dHO3bt3USqVDA4OsrS0dOjORcVwEVwm57UsZ2dnh8HBQVQqFXfv3hNiiUAgEJxxhGAiEAhODZfLhcPhOO1lnDmy5QtUVVUxPDx84mspppVwPndJqlhyUCRJorOzk+1tF2NjY5DTb7NLrrKdTIrJOCmmm85xZpvsluio1Spu9XcxMjpK6JBiRjgms+naYWd9CdnvSTtXW1NDb18fI6Oj7ByByKHWaOju7mFwcPDYWmKfd5RKJS0tLdy7dw+9Tsfg0BCLCwuHKsMqlpPomCOIEwmHmXj9mtWVVW7cuEFraysKxZtbgioQCATnBSGYCASCUyMcDqNSizfsqeS681tVXY3L5WJpubCj46jJVY6j31o+UrFEvzKX9+gzKqkL77D94hGx6RGYGS08aTGuk0zhJBtnwG2iVqm409+N5N8heEjRpLKigpn5Rfx+/z7RxGqxcPvWLVZXV5manDz0xl2tUdPT3c3g0BDb29uHmusio1AoaGxs5B337mGxWhkbHWVyYhKfz3csz3cRXCbnAVmWWV5aYnR0lObmZq5du4pWqz3tZQkEAoGgSESGiUAgODVUKhXRSATlAXMZ3iQi4TBKpRK7zYbX50On06E8wbyDUilWLNGvzKV97DcXcByZwet2Mzwxy62WWtSZoklzd/brEqJJatZJZs5JoXyTDNGkYLZJke2HochsE5UWlQosgNvtJqKwHjjTBKC5qYnBkVH6urswICPp93JGNBoN165dY3V1jeGhIerq6ig7hBtMpY6LJkNDQ9y8eVNsGPMgSRJ1tbXU1dbicrmYnp4mEAhQVVVFmcNx5B1nznuWiZ7o2RR/ZJm1tTWWl5epra3l7t17wlEiEAgE55Cz+25bIBBcaGKxGLFYjGAweNpLORcYjUYqKioYHx9namqKly9fpp3Xq092w5Avu6QYsSThGoG4SJI4isFisdDd3c3TuWUWgkBlffyAuOskcWSjWMcJ5C/ToQi3SZElOlCa20SlM2Aym8DnPlT3pERuyczCIvcfPcGzsULEk142U1lZwd27d/H5fIwdshxIpVbT2trKwMCLI8/juahYrVauXr3K9Rs3iMZiDA4OMj83T/iIBI4zKTSUwJkUemSZ9bU1Xr16BcDdu3dpaWkRYolAIBCcUyRZvGvZh9vtxmq14lqaw2KxnPZyBIILx9b2NiPDw1RVV1NRXv5GtxLOhZ5o3s3A8+fP6e7uRq/Xx8fL4bydcnQhD/4cp/W+Lfw5DIe6ndWsJTn5ynESLYNzkSqUHAZZlpmdnQUZ+vr7UKQ6blYX0gfncp5sLu39P1tnHdda/N9s3XQAPFtADrcJxN0mkN9tAhDaK7sotpPOysoKWqWE0WQkIGmKuiYXsViMra0t7BYTsZjMpi9ITW0tRoMhOcblcjE0NEx5uYPq6uoD/94uLy2hVKloa2091JrfRGRZZnV1ldnZWRQKJTU11ZgP+T4lEZx6JsWHItBr1GdD+JFlNjY2cC4tUVVZSXNz86EcYIILjuHguV4XieSey+W6cHuuxOc295EfxKI93N/ok8AdDNH4nz99Ib8Xh0U4TAQCwYnhdrsZGBhgcmKC7u5uKioqhFhyQBobG5mcnDztZewjtX1w1vNHJJZA3CHR3NyM2WLm4cNH+P3+vZMJ10mm8ySTTMdJJmfYbVJVVYXbF8Dn9aGTD7fZVSgUOBwOFGotJrOJ2jILoyMjvP32fcbHx3G73VgslmRHl6GhIfwHzNaoqqpiZXnlUOt9U5EkiaqqKm7fvk1PTzdbW1sMvhpkZXmZWPRgbqMzITacZ3aFksHBQSKRCHdu36a9vV2IJQKBQHBBEK/mAoHgUASDIebmZikrK2NnZ4fFxUWuXLnC69eviUQimM1m9Ho9y8vLqNVqamtrMRiNp73sc09ZWRlT09NEIpFje2Oez12Sj1zuklLFEl2uANYM6gBjWxvPnz2nu6d7f5vOhGiyurAnmmQ6TjJFk1S3SUI0ca3FRZMTyjYpxmnS2NjI+OvXRFbX6WyJP69fOlyQciAUASQud7YhI7O8tcP09DQ+nw+lUonNZqO9vZ3p6WkMRiMN9fVIJeTpSAoFarUKv9+fdEgJSsdoNNLf3080GmVhYYHh4WFMJiM1NTVoddmDmvNxXrNM/KEwes0pCD+yzObWFs7FRRzl5dy6fRu1EEkEAoHgwiFe2QUCwYEIBoNMTEzg3tmhurqaxcVFdHo9be3tPHz4kI7OTsxmMz6fj0AgQEdHh+iIUwJ+lPFNQI4NTEyWQZbxeL3YrCdv7c1WjpPPXVKsWJIqkgSsVUWtRedawba1xM0yI96pUXyLWgyXbuwfWKxwkisYtphQWM8W8spsYdEEcgsnGU6TQsJJZ0cHr169YnrBSUt9bbw865CiCSQ2ompq7BZs9rgoFIvG2NlxM7+wQDQWQ47FeDU4SGNjIzabrah5o5EI4XAE3QE29YL9KJVKmpqaaGxsZGtri+npaaLRKFVV1djttqJcfH6UydIcQQFkma2tLZxOJ2VlZdy6dQu1+NsmEAgEFxaRYZIFkWEiEOQmEAwy8fo1Ho+Huvp6rNk267IsSm2OgHw5JtFYjGfPnnHr5s342N2SjIPkmOTKMCk1vyRXdkkxYklCKClWJMmFjIy8NIsEmM3xri9Se2/2wak5J9kyThL5JqVmm+zmmsDRZZsUEk1k4PmzZ9jLymio3ht7FMKJXhOfI/MOfjQaZW1tjbXVVWRZRlIoKLPbMRqNmEymnALp/NwcZWUOamqqD702QXYCgQCzs7Osb2zgKCujqqqqYDey85xlclI5JttbWywuLmKz2Whrb0cjhBLBQREZJoDIMDlLiAyT3AiHiUAgKIpAIMDr16/x+nzU19XR2JRjIwhCLDkBlAoFKqUyWZLjl9RJ0eQskkssOSqhJIGEhFTTzOrqKqPzq9yor0AxMbx3PlU8KeQ4KaZMJ5vbZLdE5yTdJhJw9do1njx5gmq3bEavVh6J2ySxgdbvvt9LbEyVSiXV1dVUV1fj9XhwOp2sra3h8/tZXV0lHA6jUCgwGAxIkkQsJuP3+9DqdFRXH833W5AdnU5HV1cXHbEYS8vLjI2NodFqqa2pyVkSKVwmuXFtb7OwsIDVauXGjRtoNGd/8yMQCASCo0EIJgKBoCCrq2tMTk7Q0NBIU3PzaS/njSJfroC9rIzl5WXq6+tPeFX7yecuOSmxJJXKykp0ej0PZma4cvkyJpMJeWkGeVc8KVk4SZTpZLpNrBX5s012RRPI4jYxxB0wR5VtopAkbly/zuPHj1EqlWA2H5loAnslOnqi++7mG00mOjo7iUajrK+tsb6+jt5goK62NlmuoFAo0On14q78CaJQKKirraWutha32x3PovH7qaqsxOFwZM2eOY9ZJseVY+J2uVhYWMBsNnP9+nW0Wu2Rzi8QCASCs48QTAQCQUE2NjdoamrCtFviIDgZCt3xdZSVMT09fSSCiZ5IztbCx0EpYoluNkv3mjwEmuKihsVspqurk5evXtLe1k5lTTNA8cJJKW6ThGgCh3ObFBJNyO82USqV3Lhxg8dPntDa2gq7bYH1uxrFUbhNcokmieevqq6mqroav8/H8vIy7p0dyh0OGhoahFhyilgsFq5cuUI4HGZufp7BwUFsNhvV1dWodx0TwmUSx+vxMDc3h9Fo5Oq1a+iEUCIQCARvLEIwEQgEBfF5vVRVCQv9WcNkMhEMBpmamqK+vh6VFEGvVufNMcmG32BH79sqPLBEcrlLihFLUkWSQKGcj9TrNpxp1+qA3t5eJiYm2PHs0NrahlSscFKK26SYTjqFRBMoXKIDBd0marWaG9ev8+TpUzo7O9FptfjD0SMt0cknmiTQGwxxR5oss+1yMTIyQjgcpqamltraGhGUeUqo1WraWltpbWlhbW2dqakpJEmiuroGi8UM0vl0mRwFwWCQudlZFAoFly9fFl2cBAKBQCAEE4FAUJhQOHxsrWsFhcm1eZEkies3bjA/N8fIyAh+v5+vv339eNeSI/C1VHKJJQcVSnJdo9twYpyf4opWgX9zmRc7O1y+dBmFQlGccJKtTOeQbpOjbj8M+90mWq2Wa1ev8nxggO7ubjS7QlpCNIHDuU1y5ZpkRZKw2WzYbDZi0Sjr6+s8ffoUtVpNQ0MD5eUVKBQi9+ikkSSJysoKKisr8Pl8TE/PMDc3S0VFBU1V5ae9vBMlGomwsLCAz+eju7s7e5i5QCAQCN5I9hevCgQCQQZ6nQ6/z3fay3gjKVSTr1QoaG5u5sqVK9TU1uIP+HOODWhM6I+hkUS+dsKZpLYN3nduVywJOGoPJJZkIzFXwFGLXqenIxZg++FX8Pq8yTFSTfOeeDIxnBRPgLhokuk4SZApnCSwVuwPhU0lQzjZh8EcPzace46TbGgM+8p00qYxGLh06RJjo6NEorsdUMLRpAPpKEKCk8JJkWUcCqWSyqoqevv6aGxqYn19nQcP7jM4NITb7T70egQHw2Aw0NfXy507d9BqtXg8HqLhIBsbG8RisdNeXtGUWk4kx2IsOZ2MjIxQWVXF7du3hVgiEAgEgjSEYCIQCArS2NTE7OwssaiobT/LVFZUED4lG31m4Gu+sNds7pJUseS4CDhqiVU3YzQaCb54gm/gUdr5TOEkjVTRJFU4KauJH/OvswsnUFA0ySucQH7RBNJEk0zhxGI2093Tw8jICIFAIPn4aYomCbRaLfUNDfT391NmtzM1NcXbb99ncnKSYDB46HUJSkepVNLY2EhZbQNmk5lYLMbIyAhjY2Osr68TPcPiSUklRLLMxvo6g4OD6HR67t27R1VlJZLo8CYQCASCDITHXiAQFKTMbqexsZGhoSEaGxux2mynvaQ3jmIyBfR6PcGAB51KQSBy9jY2udwlxYolqqnxkp8z0tq577FQRT3K8hjhxSm2H30Fi9mCoudK8nymaJIs0ykUCpsv2+SE2g9nyzYps9u5dOkSoyMjaDQaGhobUatUR16iU0yuSVYkCZPZjMlsRo7F2Nzc5MWLFwDU1dVRXV0d7/ojOFGUSiWtTY20trYSCARYWlpibHQUhUKBo7wcu8127ko1w6EwG5sbbKyvU1ZWxp07d87d5yAQCASCk0WSZVk+7UWcNdxuN1arFdfSHBaL5bSXIxCcGcLhMCMjI3i9XkwmEzabDbPZjCRJ8Ttz4u7csaEnWtQd1Knpadqqy5GV2Te/upAHfxYjgN63lbVLjm5nFb9StzcuS4ZJtpbC2RwmOud0TndJLrEkVSQJVDZkHZMP3ep82seZAsr29jaG7RXMZhNqlRqp+3LaeXlpJvn/tHyT1ZQypFThZHNp7/+ZLYhda/F/MwNhATx7obtZhROICyeQP9sEIBQvn8sWCLu+scHr8XFsNhu1dbUopLjRVK+OCxJH0X5Yr4nPcdgWr5FwmLW1NTY3N9Hr9TQ2NmK324UL4ASR/R5Qpr8uBINBnEtLbKyvEw6H0Wg0mM1mLBYLBoPhVL8/eo16389dMBBgfWOD7e1t1CoV1dXVVFVVidBhweliEKVfkLLncrku3J4r8bnNfeQHsWg1p72cgriDIRr/86cv5PfisAjBJAtCMBEI8hOLybhc26ytreFyuZBlGZfbzZ3bt4VockwUK5i4XC4MUgS1zpj1vC7kAdgnmhy3YJKrM04xYslBhJJspIonqcJJKBxiYmKSPpsOoyH+dcslnKSJJrAnnKSKJrAnnOQSTSCvcFJQNIEDCycysLi4yOzsLNVVVVRUViJxNkWTBH6fj9XV1bQWxYbdlsmC40P2x18vMkWTVAKBAFtbW2xtbbGzs0NMltHpdBgMBoxGI0aD4cRcHHqNGr+swOfzsb6xgdvlRq/XUVNTQ0VFhXAqCc4OQjABhGBylhCCSW6EYJIFIZgIBKXz8OFDenp7Cw8UHJhiRBOX241BDqPWZxdMoDSXyVEKJtnEEsheinPUYkkmmeKJjIzT6WTHvcP1KhsKRdx5kSqcFHSbFCuaQFFuk5yiCRyJ2yQaizE9PcXq6hoNDQ3YrNakaAKHF06OWjQBki2K11ZXRYviEyKbyyTveFnG7/fjdrvjx84OkXAYSZLQarWoVCqUSmX8UKlQZfyr3j1f7HMFgkH8Ph9+vx+/309TQx3ODRc1NTWUlTlEBybB2UQIJoAQTM4SQjDJjSjcFAgER4JKpcK1vS3yTU4RGRgaHKS1tZUWizIZ7HmU+O3VBVsL5wt8zeSwYolqfCznuUhnV+7n3Z1btzqffL661k48Vg8Ppqbo7OikPOhGHn2ZFE0y2xDvyzY5aPvhTNHkhNoPKxUK2tvaaWxsYnx8nCWnk8amJowGQzLb5NRyTXIhWhSfeSRJwmAwYDAYqK6uTj4ei8Xw+f1EIhEi4XD830iEcDhMyOcjHA4TjkQIh0JEo1FkAFmOl3wqFMiynDykjOcymkw4HA6MRiMGrRpHbeNpfOoCgUAguIAIwUQgEBwat9vN9raLQDDIJSGYnBoSoNPpqKioAM5e6GsxFBJLMgWSYG1uF4Y2i5iSKaJkCic2oK+3j8mpSTaMRrq6upBHXwKkCScJ0QQyQmGPKhAW4sJJoUBYOFQgLIBGraa/rw+fz8fI6ChKpYKmxibMeu3ZFE12SbQorqyqIhgMsra6ytTUVPKcyWjEZDIlD43m7N/hO4tIelPJLpNsKBQKTMbczrdcxGSZWDSKQqHYy8vKRzRywBUKBAKBQLAfIZgIBIKSCYfDvHz5kkgk/sY0GAzS0dGOzW4/5ZVdbPwo0Wvyt8+02e2srKxgqqk4wZWVTrbskmLFknwiSSqZ47TO2TTBJVU8SRVOtLOTdHZ04lxy8vz5c65cuYLCOVO82yQhmkC62yQhmkBpbpNd0QSOzm2SrUTHYDBw4/p11jc2GBkZobKqiqa6miMRTYD4z+4RiyYJEi2KE8SiUfz+AH6/j6WlJfx+P+HdshCr1Up5eTl2u110SDkHKCQJhfg+CQSCc4raqEGt1Z72MgqiFi+zORFfGoFAUBLRaJSnz57R0NCA2Ww+7eUIMrCYzayvr0NNBXp19rKcgMaEPkeOyXGQq51wNrKJJaUKJblIvT5VPMkqnEyN0wisOsp5/Ogx165fQ6vR5nWbFGw/nCjRyeU2SYgmcOzth7OV6CQodzgou3ePqakpngy8pKW5mXJbvJ758G6T3f8fk3CSQKFUYjQZMZrSHQ1yLIbH62VtbY3JqSlkWabMbqe5uRntOXhDe6pEI4d2mQgEAoFAcN5QnPYCBALB+SEWizEwMEBNdbUQS06RRJhmNiRJiocuHnBjq+d47OzZ2gmnkto+OO3xIxJLMgnWNiXnVI2P7Sv1SQgnlVvr9GsUPHnyhB3PDlJ9G0BSOIG4aCLVNCNPDCfLdOIXZ2SbJMjMNklgrdhfppNKRrbJPgzm/WU6ucgQTjJRSBLtbW1cvXqVRaeTwbHXxOQYerlwp6Z8JN0mnJBal4GkUGA2m6lvaKC3t5fe3l70ej0DAwMMDAzg3tkpPMkbiKTPnVl0Fkl29xEIBAKB4JCIWwUCgSAvsizz4sUL/IEAElBZWYm9rOy0l/XG4keZd7OZCEc80NwGO3rfVtpjAXMl+oxOOYcl0R0nk1ylOIXEEmlkNO/5VOSe9E42ibmzOU5Sy3Su6iVevHxFR2cHFRmiSVFuk9MKhIX8bpM8JToAep2OG9evs7a+xpPnL7nS34NOA4EzmmtSKpIkYbPbsdnt+LxeXr9+TTQSoaWlhfLy8sJ5GW8a58FlolSJHBPBG43L5eKjH/0oH/vYx+jv7z/t5QgE5x7hMBEIBAXZ2trCarHQ0dFBecXZzsZ405EgTTBJbRObif4U96rZuuNkohofyymWSCOjySPU2FrUkXldKvkcJ4HKBkLVTVwzaGBsmLm5ufhcOdwmwNG5TZzTx+s20RhAY0B2reZ0mwBUlFdw9+5dFlfW8ezsoIkEcs9ZBKftNMmGwWiko6ODtrY2lpeXuX//ASsrKwcWIC8a581lIhC8iYyOjvK+972PK1eu8IEPfIBw+HCuQIFAIAQTgUBQAEmSeOc734lSqWRzc6vwBYITIbMsR5ZlnEtLvJ6YoKoqXv6SrywnkKct8EmTrRwnX7vghNiRKoQUQzHiSaCmkWG9jYfr28w8fY5yLOVcVSMmkwnj4iyjo6PIyEj1bUj1bcijL/ccJ7slOsDhRBMoXKJjsiOvzOYXTuDQJToQ73LS2dGB1mjmxfAoss+FLhbKP28ezqJoAqDWaGhqbqanp5vV1VUePXqEy+0+7WWdHYR7QyA4s2xvbxMKhfjc5z7Hj/7oj4pga4HgCBC/RQKBoCAqlWr3OLgNX3B0ZCvLmZufZ2tzkxs3bqBUHE4L1xPBf8J/HrKV42Rzl6SKJYch9XrN3FRy3snqGr40s0AoqkATjfEe/LSPj6WV6SiAytlxXI82sd1+R3xd9W3IC5P7OukceYkOHG/74QIlOhDvpnP71i2WlpaIhCPo9HqiidbFJXKWynMyUapUNDU3EwwEeD0+jkqloru7G53u6MrTzhvJFsMCgeBMcvfuXT7/+c9TXl4uxBKB4IgQDhOBQFAUNpuNhYV5NjY2QFjUzxwOh4OYLGcVS/KV5WTiNxRuDe23V6MP7W2a/OX16P2uop+jELncJaWIJeGh0bQjH8GGFkYt5TzecjP47CVB5xLtZVZC0RjL5jKCtU37ynSkpk6i0Sjuxw/2HjuJEh0oWKJzKLdJkSU6ADU1NVgcFUQjEaI7WwSDwbzjc3FWnSYJtDodnV1dVFRW8vz5c8bGxohGz+ZaBQKBoLq6WoglAsERIgQTgUBQFA6Hgzt37hAKBnn16hXDQ0OnvaQ3Gj/KtLIchSTh9/mIxmLp4wqEcxaTYxIwV6KPHi6zolQy3SWFxJJsAkm4pZ1wS3vW86kiyrRrh79fWOQ+agaVWnaiMSZGX6NZX6dMr0tbT6pwomjuYsQfYefpQ/wvnuJcckJ966mX6ECBbBM4khIdAJVSidnuwGgyogx6WVxcJHYAQdUfCsfdJmdUNAEwm8309fWh0Wh48OABc/Pzb2S+iaQ3nYuyHOGEEQgEAsFRIORHgUBQNCqViq6uLurq6hgeGTnt5bzxRCIRZmdnqW9oYGNjg8rKypLKcQIaE7rQ8Wwq9Dsb+M2OI50zm1iSKnwkxJF9Y7I8rp6eSF676t4hZDDQZrMyseWirqmZWrORqs1VWpecYLcC+zvqRDq76Gjv4NXYKI1yEJ3Xy9vT0zTUN1Df0IK0OP1GlOgAqHVGbDojOp0Lv3sTj6TGYrHkvSYb8RKd3f+fsRIdACQJR3k5ZWVlOJeWuH//PnV1ddTX16NUnsH1vqmITjkCgUAgOCKEw0QgEJSMWq1Gp9UyNDjI+traaS/njSISibC4uMjTJ0/w+bw01FYzMDDA2toa4UjuDUIpZTkQzzE5KP6qxgNfm41cbYOzOUmKJXFNuKUdu1qNcm2d8bEJtCoF/eVl3KyqoKGnj3BT275g2FS3iSRJdHV2MbgTJBKJ0BkLs7Kywv3795kOScjIp1eicxSBsClOk0JuEwnQm63YbDZMyhivX78+UIeGs16iA/H23XV1dfT29hEKhXn48CGvXr1ieXn5wKVJ5w4hSAgEAoHgDUA4TAQCQclotVouX76Mz+djcHBQtBo+bmQZt9vN/MICfp8Pu72Mq1evolSp0BOlo70djUaTM4zSL6nRy7k3rnol+FP2pn6DHb3vZDoiqabG0wJfc7USznSXpIolh6W+p4937OywFQxSsbZC/cIi2PbcEaHG1rRgWLmne89tsluec+PGTUaGh+nobKVhbgKLxcJiNMqD+TV6e3uxJspzui/viSbZ3CarC3HRJOE0gbhosrlU2G2S6TSB4twmvp24aJLPaQJFu00UGh1Wi0S/Ts+TkTEqKiqoqqpCyntVOmc5DDYVhVJBVXUVVdVVeD1eXC4XCwsLhMNhNBoNFRUV1NbWXrg8ARH+KhAIBII3hYv1F1wgEJwYW1tbjI2NUVubY5MlOBKWlpZYXFhAo9HQ1NSE1WbbN+YgpQ8JSinL0UcD+JV7oow+5MF/zO2Jc7lLoLBYEniV+1rdpT1BQpIkWiwWWgAqKogA0q4go+6Lj0sINgnhRO6JPx6sbULrnEU3OUF3dzcjoyNotVravF6sOx78tjLGxsawmC10mdVw2BKdhHCSKZrAmSrRQaVFA9y73MvU8jpDg4O0tLRgNBrzX5fCeRFNEhhNRowmI7vfKSLhMBsbGzx+/Bij0UhnZ+fF67ATjcTLXwQCgUAguKCIkhyBQFASsViMx48fMzMzQ1t7OzZ74a4qgoMTDASwWCxcvnIlq1gCpIW/5qPUspxUAub0DbLfXr1//iPslJNKNndJLrEk8Go0eQDI7e1Zj9Rx2YSVzLDYzLWklukk3Cb66SmuqdUYDUaWNWbsZXZaQ36i0Sgrqyu8WN+JrylHiU4axxQIe5QlOgVRaZEkaKsp5+q1aywuLjI1PV1Sh5nzUJ6TC5VaTVV1NX39/ZSXl/Ps2TMWnQXCds8Rkv54xVKBQCAQCM4CpyqY/Nf/+l+5fPkyFosFi8XCvXv3+Ku/+qvk+c997nO85z3voby8HEmSGBgYKGn+z372s0iSxLd/+7cf7cIFgjcYSZIIhyO0tbej1WpPezkXnqbmZnw+X7ydcxaKvfNearccv8FeUo6Jv7w+57lAbQs610rRcx2UbCJJLjLHZBNPsnXZgbhokiqcQFw0CdY2ISkUNHl22NrawmWrRNd9mVsWIzfNBlqaW9JaD2d20TlQrkm2bBOIiyYF2g9n5RhEE1Ra9LEgN27coLq6mqGhIdbX1wtfu8t5Fk0SmHY77Gysb/Ds+fMDZbsIBAKBQCA4eU5VMKmvr+cTn/gET5484cmTJ3zDN3wD3/Zt38bQbrtSr9fL13zN1/CJT3yi5LlnZ2f52Mc+xjvf+c6jXrZA8MYSDIYYGh6Od4N4A9tpngqyjEKhYGtzM++wYl0m2Qgcc1nNUZLLXZIqlmRDlmVmd3YY2FhndmcnrR1sLvEk+ZwZwkmCTNEESIomV9VqRoZH2FjfQGruRKvVYlyMixRSfVuacJIgZ+vhyvq4aJIqnJTVHC4QFnK7TQzm+LHhzC2caAygMRQVBgvEhROfiyqTlrt37xIMBhkeGSEQKK5d9UUQTSSFguaWZiorKnj06BFrJYhGZ5Uz3WJYqRI5KwKBQCA4NKcqmLzvfe/jW77lW+js7KSzs5Nf+qVfwmQy8eDBAwC+53u+h3/7b/8t3/RN31TSvNFolA984AP8/M//PK2t+9tQCgSCg/Hy5QusFgs9vT0glRLhKDgoHq8XgPaOjpxjSsl3OExZDsRzTNI+zpN/ot/J7oo5agqJJQBzHg9vrSzzYmODt1aWmfNkX3ch4QSyu00yS3QkhcRVtYrXExOsrKwiNXfuXvsiOV/RogkcW4kOnLDbBFAGPXR3d9PX28vU9DRz8/PEYrGCl18E0QTAYrXS29fH3Nwcg4ODJZUoCQQCgUAgOFnOTIZJNBrls5/9LF6vl3v37h1qrl/4hV+goqKCH/zBHyxqfDAYxO12px0CgSCdWCxGOBzJmaMhOB68Xm9JQZn5KLUsB9LbCxeTY5I8V0JrYd3qfNbH8wW+JtdUhFgC4Bwdx7u0TNWWC+/SMs7RcTxDo3iGsj9HpnCSoFi3SbC2iWBdM1eUChQjwzgXnQcSTYou0SmryV2iY604WyU6AD4XJpOJO7dvY7VaGRwawlXE396LIpoolUo6Ojowmkw8fPgQv99/2ks6HGfVZSIQCAQCwSE5dcHk1atXmEwmtFotP/zDP8yf/dmf0dvbe+D5vvrVr/LpT3+a3/3d3y36ml/5lV/BarUmj4aGhsIXCQRvJDKDr14RLNJGLzg8Pq+XUCiMHIshx2I570b7UR55WY7fUHqgb6nBr5HWzrznE0KELMuMPRngocnKtNu9r6QmGwlBxDM0irW9HXVlFXMWC+rKKqzt7dDZuW9cJpkhsQmKLtGpa8ZkNKF5/ZrZ2Vmk5k6k5k7CQy+Swkku0SRniQ7sL9GBw7lNKFCiA8cimgDU19Vx69YtNtbXeT0xQTiSf/N9UUQTAIfDQXt7O8+ePWNre/u0l3MgRPirQCAQCC4ypy6YdHV1MTAwwIMHD/iRH/kRvu/7vo/h4eHCF2ZhZ2eH7/7u7+Z3f/d3KS8vL/q6j3/847hcruQxP5/9bqdA8KYSDAb5u7/7OwxGI5WVlahUoo3kSdHY2IjRaGBgYIAXL1/y9OlTlg7RacMvqY+8LCc5d57g10x0+TbfWZh27fDlbRfP1tb5inOJmZ2dvOOT4kdnJ3R20mA0caeyiktlDu5UVtFgNKWdLySexNramAkGePDgCaNPBpBlOek2ySzRgYwuOnVNGE1GTDMzTE5Mxs9nuE0SuSapYbBQINcETq5E56hzTTJEE41azZUrV2hqbGR0ZIS1tTXypSRdJNFEp9fT09PD6OgozvPcRUe4TAQCgUBwATl1wUSj0dDe3s7Nmzf5lV/5Fa5cucJv/dZvHWiuyclJZmZmeN/73odKpUKlUvEHf/AH/Pmf/zkqlYrJycms12m12mSnnsQhEAj20Gq12Gw2amtqqKyqQikEkxNDpVbT2NTEtevXuXr1KjeuX2dhYTHn+MO4TKD0shzIn2OSSqJTTqApdx5LLjaDQUIxmVaLhVAsxlYwSODVaFZ3SZpYsoskSTSazFwqc9BoMiNly+DJIZ7AbgaKVsuAXs9X3W7Gnu6V1WS6TXJ10TEYjdidToaHhpFl+XhzTc5DiU6GaAJxx8Xdu3cJh8OMDA/nDYW9SKKJSq2mt6eH5eVlxsbGisp0OUsIl4lAIBAIzgre3fy/o+LUBZNMZFkmGAwe6Nru7m5evXrFwMBA8vjWb/1W3vWudzEwMCBKbQSCAyDLMsvLy4RCITyeo30BEpSOUqVCpcruEDls+OtBynLy5ZjAXvBroLal6LVlo0yrRaOQmHK70SgU2HO0tM4mlhyIDOHEOTpOJBaj2Wwm7HCwHYmUXKITrG3CVVZF2ZKTtS99ianJKaJ1rbvXFRZNCrYePoYSnawctWiy20EngUKhoKuri76+PqamplhYWCCWoyvXRRJNJIWC9vZ2lEolDx48YGMjf2csQXGITjkCgUDwZlFVVcUHP/hB3nrrrSOZ71QFk5/5mZ/hK1/5CjMzM7x69Yqf/dmf5e///u/5wAc+AMDm5iYDAwPJEp2xsTEGBgZYXl5OzvG93/u9fPzjHwdAp9PR39+fdthsNsxmM/39/Wg0mpP/JAWCc4x7Z4e33voqGxubdHd3U15RfKmb4HgIh0JEIofbHBYKfz0siRyTUoJfC9FiNfO1NivXK8p5Z20NzWZz7sFFiCXu4ZHkkZdd4cSqUhFbWWNmZweVQoGttW1ftkmhEp2AP8DExASKth7MZguOlWWePHnC0E6IaDRaUDSBky/ROdFcE58rTTgxmUzcuXMHo9HI0OAgOznKsPyhMP5Q+EKIJkgSlVVVdHd3Mzc3y/PnAwSDodNeVVGcyRbDSuGGFAgEgjeNz3zmM7hcLr7xG7+Rzs5OPvGJTxyq5PVUBZOVlRW+53u+h66uLr7xG7+Rhw8f8td//de8+93vBuDP//zPuXbtGu9973sB+Of//J9z7do1PvWpTyXnmJubY2lp6VTWLxBcdIwGA3q9Do1Gg0p9vJtsQXFMTk7S0JA7K+Sw4a9QXFnOUeSYFIssy0y7dtgKh7FrtTSbs5fU5Op4kyBTJFF3d6Hu7kp7PJeI0tDXzx2TmU6Xh2s7PhpNcTdOrk46kF6iI8sywYEBenp60Gg1BOua0Gg0XFGrKCsr48WWj6drbnzPnxws1wROvkSn2FwTDhYGm6CxsZGbN2+ysrLC5OQkkVzBxxdFNCFeotPW3k5lVSVPnz5hYWHhtJckEAgEAsG54H3vex9/+qd/itPp5Ed+5Ef4zGc+Q1NTE//4H/9jPve5zxEpEC6fiSTLOXyubzButxur1YpraU7kmQjeeGRZ5sWLF9jsdsrKyk57OW80Xo+Hl69ecf36dbQ5SlIgXp6QKFXIOUaOn/eH928wdSEP/oyH9b4t/OzdrdXtrOJX6vbOby3j3y3p0a8v4Ndb4/9fmcNvdsSvcU4TsFbF/z/7moCjFgDV1DiBynjJpGp8jGBtE7BXyjJqKefvFxbxO1dQllfwztoaWiyWfRkmnqHRrO6SVAFE3d2V9+sCEB4dS/7f0tuzf8D4OACmvu7kQ9LERPzzurT3mHo6/piqt5tnz57R1NRIrc+N3LM3RuuMCxGRzi6CoRBLTid1UR96nR7jjVsopPh9DXlhNyy2+3LyWnlpJv5Ye0ZnudXdzXVzN/vY3L3B0JAlR8a1Fv83W/mUZyv+XFVN+88B+HbdH7vf032EfPHrrfszcPYR2S3LNVj3nVpbW2N8fJzaujrKHY6sl+s16pLK0846siwzNTmF2WyivUD77NMmWf5ylpwd0YjIWBGcPbK8vr2JJPdcLteF23MlPreln/oRLHnes50V3MEgNb/6Xy/k9wLgP/2n/8S/+lf/ilAoRHl5OT/8wz/MT//0T2MwGApee+YyTAQCwdlCkiT6+vtZXFhA6Kuni1arpamxkYGBgZzthRMUcpkcd1lOKqk5JqnBr6mdcnSre93JEiJCQljYDAYJRWO06XUFA18zyXSTFEPq2KyOkyyhsNnaDyecJpsPH1NRUU55eTmhxtb0Djq74pBqfAytRkNzczOKli6i0SjrX/0yjx4/YnJqkp3d8pmic00q689WrskhOuikUlFRwd27dwn4/YyMjGTNPLtIThOIvwa3tbXi9/sZHh4+06/DQpgQCAQCwVlheXmZX/3VX6Wnp4ef/umf5p/+03/KF7/4RX7jN36DP/uzP+Pbv/3bi5pHCCYCgaAgapUKs9mM3+c77aW80ajUamrr6qirrWV2ZibnuMOGv8Lhy3JKyTGJtO65QiKd+0WNMq0WjVLBpD+QM/A1XzlOsUJJtutyCicZobAJMkt0xlEiSRLVO3u/O9nCYCEumgAoFUrkxnbMZjOX1AqUSiXT09M8WFjF5XLhe/4QX8rv4pGX6ED2Ep2TzDXJI5oolUp6enro7unh9cQETqdzn4hw0UQTJImm5maQJMZ3HU5nmrOWZSIQCASCN4bPfe5zvO9976OxsZE//uM/5iMf+QiLi4v84R/+Ie9617v4wAc+wGc/+1n+/u//vqj5hGAiEAj2EYlE2NjYIBQOM7+wwJMnT/D7/UXZ1gTHT3V1NZubhTtoHNRlctBuOcW2Fy6VFquZr6+v44bZmD/wNaMcp2Cga5FkCifZnjObaOIdGGRzaxNN/2Uiu2GwCQqJJkC8TEmSqNreoLWllf6+fqI1TcTkGKHBp7x9/z4DLwZYXFwk5Ig7R4oWTSC32ySRawIHyzWBYxdNACxmM3fv3EGr1TI4NITHk/7zlxBNLpJwUl9Xh8frTQu/P2sIl4lAIBAITpMf+IEfoLa2lq9+9asMDAzwoz/6o9hstrQxra2t/OzP/mxR852hIlOBQHAWWFxcZGZmBlmW0Wg02O12WlpaUR8ySFRwdKjUaoxGI1ubm9hz5Mr4UR56o6hXsi/L5MBz7Wwks0xKRZIkWm0WGixmwiXW1RbjLtkYShcZHH29WcdliibJfJPOThgfxzM0msw1kdvb8Tx9QrfJlMwhCbe0w65oou7rJtTYimZuCmlkFLmnOymaaHdFk0hnF4HKBnSr86imxom0dqJRa5BrWlACtzacxGIRZn1enC+dhMMR+hwmtEPPUXT07XWGyxRNUrNNymriuSbzr/fnmlgr4rkmzun9uSYmO3i2kFdm9+eaZIom2XJNNAYI+ZBdq/lzTVTaeKaJz5W15l+SJJqbm6muqWFocBC1Wk1jYyNKZdwi5Q+F0WvU8Vyfi5BrIkm0t7UxNDSM0WjCbBbiREGUKmS/Rwg5AoFA8IawtLRU8CavXq/n537u54qaTzhMBAJBGjMzM/T09HD5yhW6e3qoqq4WYskZpL6hgZmZGba3tnC7st+BL5ZsZTnZXCZQfFkOZG8vnJpjAqTlmJwkG0PDyUPb05U8Ms9lI6vbZLdEJ5FrMjU9zZq9DKVSVbCDDuw5TWC/2yRQ2UCgsgHV1Diqqb1yjICjFoVCQUvQQ3dXN/39fQSslcyGJLyvnrL5+Ku8evWKpaUlQqHQ4Ut0MjmKXBMO7zQB0Gm13Lhxg6qqKoaGh9nc2kqeSwQgXxSniaRQ0NnZwcuXLwiH84c7A4RCIbxeLzs7O7hcLra3t9nZ8eD3+wlHIseXiSLKcgQCgUBwCpjNZlZX97+32NjYSN5QKQXhMBEI3nBCoRBqtZptl4vZmRmUKhWxWOy0lyUogNlsxma3s7K6itvloqOjA5s9vWwm3mKYvB1z/JI62TGnEH6DHb1vK/d5e3WyW46/vB79ev5WqIGmDnSz8Q16pLUTXUq3nGKQJiZyBr/mK8dJCCEJgSSVzMdSRZNU50k+t0l0ZASNc4Xae7eRFQqkiQkCr0aTHXTCLe2opycID41mdZpAXDTROmdRjY8lc10y3SYQF010G85416GmDgwGQ/Kuim57hY5YlLmdHRYXFglHIpiMRjpsejRTwyhaM5w0udwmmaJJqtskQzTJ6jbx7cRFk8M6TSCn0yRBVVUV5eXljI6Osrq6SmtLCxqN5sI5TTRaLc3NzTwfGODWzZtZ22xvb7sYHh5CrVajVqtRKBRIkoQkSURjMaKRCNFodH+AtCThKCujprYWk9F4oPVJetNexxyBQCAQCE6QXDcCgsHgnvu2BIRgIhC8QUQiEdxuN2VlZQQCAV5PTLC1uYksy1htNmpqatCLnJJzQ0tLfOO65HSyurq6TzApBb1ambXFcDFlOfpoIK3F8GGJdHahTWkvrJmbSjoxEugudac5N7KRrRwnn1iSjcS44MgYG0PD+8p11N1dhEfHcA+PJEWTlwE//UYjvpFxTH3daUGwJyGaJAjYqtC5VmiRAwS6u0AGf8DP/LaLioCb2PP7qJRKPOV1lDvK0Wi1x1eicxSiCcSFk4TTJIdwolQq6evrw+VyMTw8jMPhoLq6+sKJJmaLBavHw+TkZNZ2w/Pzc7S2tmIoUfSQYzG2t12Mj40RCARxOMpoa2tDpTrAW8Zo5Gy1GBYIBALBheW3f/u3gXi57n//7/8dk2nPLR2NRvnyl79Md3d3rstzIslnuT/dKZHsCb40dyH7UAvePGRZxul0MjMzQywWw2q14fP7qK+rw2K1gixDljuUgrPPwsICS04nXV1d8e9lBokyhHwuEwC9HM4qmOhCnn2Cid63hT9Fb9ftrCYFk4TDBEg6TPz6+Lr0K3P4zQ50u06FgLUqvsnf3USrUhwmqhTBRBoZTQom4aHRZFlLamthz9BoWuire3gkp2BSrFiSjeBIvEwmUzgJj+7mjlRX4nQu0d/fD7vdTBK5JhB3xQBJ4UQ9Hf9YvTtGMzcF7LVUhr02y6kdhBJtmFM7DCXKm1JFE2CvlXOmyCGDxrVGOBJmLCATCoUwGIyUl5dTrQGlUrFfNIG4aAL7RRMAT9yBtE80gbhoAtlFkwQhX2HRBOK5JpDXbQLx177p6WlWVlZoaW3FaDAkw5AvgmiCLDM6OkpHRwf2DMH0q2+/TX9/f1b3SSnzb2xu4lxcpKenh7IcmUk5L/d7zoZgEo2IDBPB2aLAa9ebQnLP5XJduD1X4nNb+qkfwZKlu99Zwx0MUvOr//Vcfy8SNxJnZ2epr69PK7/RaDQ0NzfzC7/wC9y5c6ekeUWGiUBwgYnGYiwtLfHw0SO2t7fp7++nvb0dh6OM3t7evQ22EEvOLTarFUmSMOfoHHPSm8LUbjn+8vqsY1I37oGmjhPLMTmsWAKk5ZykkhBnvGPjtLe3xR8sou3wQTJNgD1hKSPTJOCoRTf7OlnqBHFhKmCtQuecTopV8SeBkK0CnVbLFauO/v5+amtr8Pt9PJ918nhygdDk8NHlmhTZQUd2rR5JrgnsBga3tnL16lUW5ueZmZnBE4iLLRci00SSaG9vZ3h4eF+eSU11NWtZarhLnd/hcNDT08Ps7Cz3799nbHyc+YUFVldX2d7exufzEcmXhSKyTAQCgUBwAkxPTzM9Pc3Xfd3X8eLFi+TH09PTjI2N8Td/8zcliyUgBBOB4MKysbHB2199G5fLRUd7Ow2NjUgKBUaTCfM5VY4F+zGZzdTW1vLi5UvkPNkzhVoMQ+7wV30WzSU1/BXIG/5aLJHWzqRz4iyTCIjNDIZdMRvZLLMTmprZG3zCogmQdOykiiYQF06AdNEEkt2L9Ctz6HQ6qqqq6OrqorW1hdHVLba2tglODKZ/ERKth53T+4WTQqKJwRwXTU4gDDaBXq/n5s2bVFRUMDQ4yOJKfO6LIJqo1Gqampp4+fJlmmjR1NzMyspK3teFUp6jrb2d3t5ejAYDoWCQzc0tFhYWGBsb49mzZzx4+JAHDx7w8OEj1tbWkGX57Lg6djvlCAQCgeDi86UvfWmf6/IwnAGfpEAgOA4MBgM6nZbaurrTXorgmKmtq0OSJJ4+fcrNW7f2nS+mxfBhwl8D5kp0O+mbW33Ikxb+6tdb8Vc1JstyIF4qktjEZ5KZY3IW0fZ0pWWbbKyv097RgdpgTA+DzdF2ODUMNlumCYDmAJkmkCfXxBrPNdE5p9OcPqmiCcQ7G2k0GlpaWohEIoTc63hfPCJa00JFZcXeFyFXrslJhsHmaTucSXV1NeXl5YyNj/N8ZYUr/X3oFee/PMeya2mfm5ujqSn+tVYqFDQ0NLC8vExNbZ4yqBKQFIqspX+pRMJhFhcXmZycpKOjgzKDVmSZCAQCgeBY+Ymf+An+/b//9xiNRn7iJ34i79hf//VfL2lu4TARCC4oer0+nl2y6MS1vX0kdxkFZ5fqmhpC4TA+r/dQ82RzmQBZXSa58NurC47JzNPIV5Yj93Qnsz1gL/cjFVNfdzIz5CDIsszzpwN85fFTnj8dYPHFEM6Xw2lHNlJLdKLRKBp13Mmzr/XwAZwmEHebFOM0ydV2GPI7TfK5TRKoVCooq8ZsNqFZnePVq1fpnVWOq0TnGJwmEP98+np76erq4umLlwQCAXSc/7KRhoYGnE4nPp8v+Vh9fQPrGxvEMjvhHCMqtZqm5mY6OjpZWFjg0cshgsHA8bUvFggEAsEbz/Pnz5Olqc+fP895DAwMlDy3CH3Nggh9FVwUQuEwW5ubbGxsEI1GaWpuPu0lCY4Rt8vFyMgI165fz9o2TU/01MJfM4NfIb5hT27ed8NfExv+QGXDoYJfs4W+5sowcb4cZiUcYsJmJhKLoVIouOSooNq4V07gSxEtai/37psDYPX+Q5rfcReFtHcvIhEGm2w7nCMINhECC/uDYCEeBlsoCBayh8FC6YGw+p0NIO40yUTlWscfCKBo7MRmt+2dOK4w2FBcACgYBltkEGwqsiwzNzeH2ajHoDcQVp39YL58+LxeFp1Obly/nnzM6XSyvb1NfUPxLbuPkkg4jCoa5OXQCJUVFdTX16PX609+ISL4VXCWEKGvgAh9PUtchNDX40I4TASCC4xSEf8VD4fDuNzuU16N4LixWK0oVaqky+GoKcVlAqSFv+r98bv//qrG5GYc9jbsCTI3+glh4LiJNNYTicWoMZqIxGLshENp5w093Rh2BYtcjpM5lZKtodG0XJNinSapLZIP6jSB48k1ySRiLY+36luYYGxsDDm2e9/lnDlNIB4K29TUhNFiJRgMEvO6iUTOr9vEYDQiAVtbeyVzNTU1bJ2iy1ClVoPOxJ0b19BotQwPD/PgwQMmJifx+/2nsiaBQCAQHI7m5mYkSdp3fOQjH8k6/nOf+xzvfve7qaiowGKxcO/ePf7mb/7mRNbqdrv5/Oc/z+joaOHBWRCCiUBwQQgEAoyOjibfKI+MjPD48WM2t7aorqnh8uXLp7xCwXETi0bTSyWyUCj81S+pc4a/Zp0vR/jrUZTlZLonUstyiiHh7shElmUWvR5GtzZ5/nQAWZYxqzWoFAqWvB5UCgVm9X6HDuwXTlJRKpUo2uMumJMSTYK1TccqmvjNDvQrc/uEk6DRhslkpF4O8ODhA3bcuy6Rw4TBwqmIJgBajRZrmSNeyujfYW1tLd5u/RzS2NTE69d732NJkqitqWFtff0UVwUgUWa309nZSU9PDyqVSognAoFAcE55/PgxS0tLyeNv//ZvAfhn/+yfZR3/5S9/mXe/+9385V/+JU+fPuVd73oX73vf+3j+/PmRr+07v/M7+eQnPwmA3+/n5s2bfOd3fieXLl3iT//0T0ueTwgmAsEFYWtrm0AwyPDICFNTU2xsbtLb10ddXd3p2J8FJ45CqaSqsorRsexCwVEHW/oN6QnkAXP+komEywRIc5lAllKRjG45qeUokD3HJJVkCUwWnD4vz9dWGd3aZDjgY6epgSqDkUuOCtptdi45KqgyGPPOn000USgUhMPhrK2Hj0s0AY5cNCnWbeLXW9FqNdyotDAyMsLExMSeyJDLbXKGRRMAjd6A3Wan0mJgeGSEwDncxGs0GiRJIhgMJh9raGhgdeWQLYYPiR9lUrBVKBQ4ysqS4olapWJoaIjHT57ExapjQnTKEQgEgqOhoqKC6urq5PEXf/EXtLW18XVf93VZx//mb/4mP/VTP8WtW7fo6Ojgl3/5l+no6OD//J//c+Rr+/KXv8w73/lOAP7sz/4MWZbZ3t7mt3/7t/nFX/zFkucTgolAcEFQKhVo1GqqqqpQKpV0d3cXvkhw4WhubiLg9+Px5N4YHLTFMJQe/ppalpN8PCMXo9SynNRsD92lbqSJuHhSbPDrTihEJBajzmgiisxOOIQkSVQbTXTYyqg2mpAkqeA8CbdJokTH4XCwvLwcX3Me0SRJFtEEOJRocti2w6WW6Pj1VoJGO9crzdREPDx4+BCvZzd4+KCiSaLtcDZOQDSRVGpMRhM3+rqYmZlhfm7u3IVmOxzlOJ17X0OVSoXZYsab53XhtFAoFJSVlcVbWbe0sLKywttvv8309PTRlkeJLj0CgUBQELfbnXakiu+5CIVC/OEf/iEf/OAHi3r/BBCLxdjZ2aGsrOywS96Hy+VKzvvXf/3X/JN/8k8wGAy8973vTXNgFosQTASCC4KjvByXy0VlZSX2srKsoZ+CNwBJoqGhgbnZ7NkfxbhM/FJ2QSVbWY7fYE8rywmYK5NlOcWQWpYTaOo48rKcbJg18fKbRa8HJVLO8ptiSbhNFFtuNjc3Ce8G6+YSTZIuE9gnmmR2zoHSRBPY30EHsosmAUctutnXR5JrEnebaLlebmZwcJDpqWmQyS+amOzIK7O5hZMNZ3bhRGMAjeFYRROUKlRKFbcu9WCz2RgcHGR7e7v0eU4Ji9WCy5X+eZfZ7XgP2UXrsKS6TLKh0Whoamqir6+PWCzG48ePeTU4eOrrFggEgoOiNGlRms/BYYr/zWxoaMBqtSaPX/mVXyn4OX7+859ne3ub7//+7y/66/Jrv/ZreL1evvM7v/OgX9qcNDQ0cP/+fbxeL3/913/NN3/zNwPxfC+dTlfyfEIwEQguCEqFArVak9ysCd5cysrK8Hp9zOYQTeDkXCaQPfwVKBj+etiynFzUGoxcq6ik215Gr85QsPymGBKiiV1WpH3ds3XlAQ4smqRSimiSre0wHG2uiV9vRaFUcKPKQjgc5uGjh/h9/mMNg5Vdq/mFk0OKJiBRW2bl9u3bbG9tMz42TjgUKnjpaaNWqQiF0/8WGI1G/IHixczjpNDrj0KhoLKykv7+fhxlZYyOjvLo8WNWV1dFe2KBQCA4Rubn53G5XMnj4x//eMFrPv3pT/OP/tE/orY2R7e7DD7zmc/w7/7dv+NP/uRPqKws0AHvAPz4j/84H/jAB6ivr6e2tpav//qvB+KlOpcuXSp5PiGYCAQXCJVKiYx4M/mmIykU3Lx5g7XV1azBlUftMoHSw19Ty3KKCX/NV5YDJMtygGRZjqW3J2vwqyRJ1BlNdNvLqFJriraPFsLQ041epyO0tEYoZVOt7enKn2cCSdEkQS7RJJxRvlOsaAInl2sC0KqJ0trSwosXL+Jup9PsoHNo0QTUajWXL1+irb2NsbFxlpeWznQorKRQEMsoIzIajWciWLXULCWz2UxnZydtbW2sra1x//59pqamznU3I4FAIDirWCyWtENboCXy7OwsX/jCF/jQhz5U1Px/8id/wg/+4A/yv/7X/+KbvumbjmLJ+/jwhz/M/fv3+b3f+z3eeustFLtdQ1tbW0WGiUDwphMIBo+tpazgfBEKh5FlmZ0TyDIpJvw14TKBw4W/wv6yHN2lPfHE1Jc/uyc4kj0Q96gw9HSj0+mYefJi37liRJPMEFgoLJoAxyqaHCTXBMDu3aC3rxefz8/jR48JaM2n10FHpY0fBxRNEmGhdpuNe/fuotoNKU3mtZxFMgQdtVpdsIvWSVLM608qGrWaxsZG+vr7AXjy5AkvX73Km9ckEAgEguPl93//96msrOS9731vwbGf+cxn+P7v/37++I//uKjxh+HmzZu8//3vx2Tau9H33ve+l6/5mq8peS4hmAgEFwlZhiO6Wy44v0xMTPDq5UvqGxowm81Zx5yWy+Sw4a+HKctx9PUWPTYbsy+HmH05VHBc2bUrxGJRJh8+TT5WVOecXbKJJplk5pnA8YkmUHquiV9vxa+3YthYpK6+jsbGRp4/e87CwsLpd9A5pGgiSRKtra1cvXYNp3OR6ampMyVE5OOs/HU4TMcuhSRRUVFBX18fFeXljI2N8ejRI5ZXVkS5jkAgEJwgsViM3//93+f7vu/7UKnSg7U//vGP873f+73Jjz/zmc/wvd/7vfzar/0ad+/eZXl5meXl5X15W0dBNBrl05/+NN/1Xd/FN33TN/EN3/ANaUepCMFEILggyLJMKBQiFj1f3RwER4/b5eLmzZvU1NQcyXy5XCapHKbFMJQe/grFleUAWctyUvGN7HdspJIQSqx9PWkf56P6zm3C4che+CkH75xTbLthOBrR5CBhsPlyTfTrCzj8m/T19eF2uXj65CkhXdyFcl5FEwC9TseNGzeorq5meGjoWN70HQYZiMXSBYSjKj87Kkp1mWRiMpno7Oykvb2dzY0N3r5/n8nJScLhHFleGd9DgUAgEBycL3zhC8zNzfHBD35w37mlpSXm5vbeE/zO7/wOkUiEj3zkI9TU1CSPj370o0e+ro9+9KN89KMfJRqN0t/fz5UrV9KOUpFkIcfvw+12Y7VacS3NYbFYTns5AkHRjI2NodfrsdnthQcLLiRyLMbTZ8/o7+tHpy+cBK4nir9AULBeDuMP77+Drgt58Kc8rPdtAeAnfpdBt7OKX7m3Bv3WMv5dd4p+fSFZuqFfmcNvdiQ34gFrFbrZ10nHg2pqPB5aOj6WFACkkVFCja2Eh0aTAkLg1WjSkeEZGk2KD+7hkaQwsTE0vC+I1flyOBnamo1UsSSBa2jPFdJ0uS/ntSuPnqCosNPR0ZG8vZ8oC0p1vIRHx7D0pjzHruCTKDFKiEGp5UcJd02qcJQoV8p04iQyYDKFp0TJU6abB/ayZPaVSe06gTKzZ2CvxCrTPZQQyPzl9Xg9Hqamp2lpbqHGuHtHKnMuT/xnSapq2vcc+Hbi/zpyhMuFfPFrrQWC5CJBMFjzj8lGNIKkT3dZRSIRXrx8iV6no76+/kw4/WZnZqirr6cs5e/Bo8eP6ersRFKcjftlxbz+lEJMltnc2GBlZQW9wUBrS8t+l12W759AcOIc5LXnApLcc7lcF27PlfjcVn/hx7Ho8ueAnAXcgSCV//Y3L8T3ory8nD/4gz/gW77lW45kvrPxF1MgEBwJCoUSr9fL8tISkUiEjfV1pqemRI33G4Db5ca1vc3g4CBlZWVFiSVHTTaXyUFbDMP+8Fcga/hrallOmsskC46+3pJyTBJiiSzLrPq8TG9vserzYuntTnOc5MJsNiGvb/Hy5UsC/vjXopTOOQlKaTcMh3eaQOFck4OEwerXFzCaTPT39bO+sc7zyYW4C+I0nSaluk2yuBRUKhXXr11Dr9czMjJCJJfD4QSx2+2srqR/DdQqFZHI2SofOqzLJBWFJFFeXk5fXx9VlZW8npjg4cOHLC0tiXIdgUAgeEPQaDS05yhpPghCMBEILhAmkwlZlolEo0xOTgLQ0NjIzPQ0ckyU6lxkZmdnmJ2dxW6309raWvR1fpQFNyx+SX2iLYYhe/hrpjsiX/grUFJZTiHW/D5GN9aYdm8xurHGmj/uYigkmhh6ujGZTFgiMoODg0xOTBLd3bCmluaoujpZCAV58PQpc56d5OaumBBYOLxoUmrbYThYrgnERRNJIdHc3ExVVRUPhicIBoPZRROT/fhFEzgS0USSJFpaWujq6mJkZIQdt7u0OY8Ys9nM9vZW2mMqlYpo9Ox0l0lkmRylaJLAaDTS0d5OR0cH29vbvH3/PuPj40RjZ0swEggEAsHR8pM/+ZP81m/91pEJ5UIwEQguEDU11fT29tLb08OtmzdpaWmhzG6nrq4e59LSaS9PcIyoVCpa29qob2g4sefMFv7qN9gP3WI4NfxVt+E8UPhrarectFKXXVJdJrWXe/PmmLiGRvCGQkTkGJUGIxE5hjelbXDCgfL02QCvtzdZ9nrS/kgberrRqNXcvHUTtVrNs2fP2LLFxYOEaLLo9TJgNjAS8PFwdYV5rydnnkkmCdEklVJFEzi5MFiIiyYAZouZvr4+Rpc2cLndxBbSRTAgKZrsE05OWzSBrHkYNpuN27dvs7S0hHNx8dTaD0sKBZIkpeV5qNRqImcsoPYwAbDFoFarqa+v51J/P1qdjh23m8ePH7OyuipcJwKBQHABeeutt/ijP/oj2traeN/73sd3fMd3pB2lIgQTgeCCs73tYml5ie3t7dNeiuAY0Wg0jI+NMzQ4yPbWVuELMjgul8lRtxjO5jJJzfDQXerOG/4qyzL+pkbGAj4WM4SNbDRe6mU9EmYr6McfDrPi9aCSFBg1mrRxodYmJoI+Jra3eLWxxoovvd2soacb58thGpsauX7jOltbWwz5PITDYWRkXKEg4ViMeo0Wz9ISroQg07k/WyQzBBaytxs+atHkIGGwUFg0USgVtLa2soOWB8MThKazuIFylegYzPHjNEQTZTx/JZtoolaruXHjBiq1mtHRUaKR03F1WK02Njb2fq/UKtWpraUQx+EySUWSJMrsdkwmMy2trayvrfP2228zNjaGP1B86aBAIBAIzjY2m433v//9fN3XfR3l5eVYrda0o1RE6GsWROir4CKxtrbG+vo6dfX1hQcLzi1yLIY/ECAYCDA1Pc21q1dRKIu/c3uU4a8QD4BNDX8FkgGwhcJfIb75zhf+CvGNf2r4K8SFg3zhryv1dTxeW8a1tIyuuoprFZXUGU04X8adHpnhr8teD//w8iXqqkr8kQg1RhP1FisVekNax5Hp7S2m3VtonKt4TDquNTTRVeZImyvhYqm9HA97HXw1SKXXBzJs11YxI0eJyDKKtQ1uGIz0Xr26d/H4eJprptgQWCg9CBZOJwwW4q0AZ6anaS23YjabkOoyyssOGgZbShAslBbIuFvikitIdHNzk5GRETo6OtDp9cXPewT4fT5WV1e5dOkSADOzs8iyjMPhKHDlyaMn/iJylCGwWZ9Ho95ztcgy29vbLK+sIAGNjY1UVlaeuW5CgguICH0FROjrWeIihb4eNcJhIhBccEwmEx6vl1iKDVuWZXbcbubn5pifmz/F1QmOCkmhwGAwYC8rw2qx8PjxYzbW10uao5g7vKfhMkkNfy3GZQK5WwwnnBxdbW0EllfY2XVyJESMVGRZZmbHxUYkTGx1DZ1KiV2np9Jg3LehMmo0RENhVkJ+wttu5sbHmZ+fJ5rye2fo6SYajeFcdOJ2uQiFQlTevY3FasEQCqFfWcMWCHKjq4s6tSZzOQfKM4GjdZrA8YXBAiiVStra29mWVWxtbROayXj+g4bBnpLTBKCsrIxr164zMTGB23WyuSZarZZAMJj8+Cw7TI67NCcrkoTNbqe7u5vW1lY2Njb2XCd+/8mvRyAQCARHQiQS4Qtf+AK/8zu/w85O/IaK0+k8UCMMIZgIBBccnU6H2WTi9evXDA8NMTQ4yPDwMG63m6rqatzu0uv2BWeb9o4Orl2/ztTUND6vF5/XW/CaYjYrfim7oJItywQoKssk4SyA/e6D1CwT2O92yJVlkuq6yMwyMSwsolYomPfsoELCrNkvTCRY8XlZ9OwQNhuYCPoJRCL7SnESlGv1qDa36e3qptds4xtv3yEcDvPs2TNmZmaIRCLIsozHs8POzALDwyN0dcWFH11vNxWRGP+fe/fotljZmJzE6/Ww9erV3hPkKM3JpBjRRJZlJrdcPHauMKy3IcsyqvF4udLkxjaPFpaY3NjGXxH/3pQimsDhS3Qg3uFFslXg8/nYGXtFLDW0+hyKJgaDntu3bzO/MJ9843YSKBSKNLFcpVaniXhnkULCrSzLTExN8/DJMyampo8sh0St0VDf0MClS5fQ6/UMDg7y6PFjlldWRNaJQCAQnCNmZ2e5dOkS3/Zt38ZHPvIR1tbWAPjVX/1VPvaxj5U8nxBMBIILjiRJdHd3c+vWLe7evcu9e/e4d/cuPT09lDscwnp8QdFoNPT19TI0NMyLFy+KbnN6VC6TbC2G943P4zJJLevIdJnA/jKSgi6TXerUGm5VVNNnd3DFYMQxu7dJzwx/3QmH0CtVXHZUYFEqqTGZqdAb9s0J8T/OTY4KeqpqKFepmR8cobW1lVu3biFJEgMDAwwMDOB1lGE2m7h77y5GkzFtjs2hERobGrl58xax1ha8Hi8DA8/Z3NwAZOjsTHOZJL8+WfJMspEQTaafDvClmQUeO1f50swCI7vfq9lnA3xxco7H88t8cXKOyY1thpRmvhxSMf3sGcrJjO46x5hrAvEgY2VZFUqlku3hAVypOUznUDRRqVTcvHGD2ZkZvJ7CIuaRIEmkbvWVSmW6+HTGKKZrzuT0DH/791/m4ZOn/O3ff5nJ6ZmjXcSu66Sru5u21lY2NzZ4+/59RkdHhetEIBAIzgEf/ehHuXnzJltbW+hTSmHf//7388UvfrHk+YRgIhAIBBcUg9HIrdu3qK+vx+nMEYqZwnlxmSTyNRIU5TLZLcux9vVStbBIX5mDS1evZhUME6KJWa1BrVQSiEbpaGzEuryedbzH42Fjc4PWttbd50jvytPU1MTNmzdpbm6mvSOe+5HITEmg7UkXgBxlDhx3btOsUrO0tMSTJ09YXFyIu1QKlOZA9hBYiIsmm6EwoWiM9jIroWiMTX+AYG0Ty+YyIstLtDvshKNRXi6v8Scvx/jTwXH+56Kb19uekkt04HCiCUDUZI+3THfOMDY6utci/RyKJvEw2JtMTU+d3OY7xR2hVCqJnmHBBAqLJhubW4RDITraWgmHQmxslh5yXSxJ10l/Pwajcc91srxMLCZcJwKBQHAWeeutt/g3/+bfoMlwBTc1NbG4uFjyfEIwEQjecMRbvouPo7yczc3NosefJ5dJIrgUSssyCY/uuSUCw6Msej2Mbm0itzUm7fdVBiOXHBW02+xcclTgUKpwDY0kr5NlmRWPh7eGXlHR3LTPtj/7cijtY7s9/vXIDJZNJdFmOIFCoaSvr59r164T8Ad44nbj9XrTWsNmK81Jfp5ZRBNzUyua9XUmNl1olArK9PEw3jK9DqW9nMnRMXQb66x6fAyvbuAPRRhe3eBpJP5zcRqiSVBnxmQyUq+M8uDhQ3bcu1kghxFNNIZTEU20Wg3Xr1/n9evXhFLaU58ESoWCWPRsCyaQX7x1lNlRazS8npxCrdHgKLPnHHtkSBI2m23XddLG5tYW9x/EXSc+n+/4n18gEAgERROLxbKWny4sLGA2m0ueTwgmAsEbTCwWEyU5Fxyf14vb5SJQZNvM8+gyOUiWSQJHXy9LkTDP11YZ3drk+doqq5EwvpFRJEmi2miiw1ZGtdFE85X+tOdY8/t4e2KMHbWKWb+XNf/exinTZZKNQi4TAHV3F+7hkWQY6q1bN1EoFCw/eIzLnR4gmqs0J1M0abGa+YZyG7dDft7VXE+rLZ6G32qz8K7meq53d/ONNeVU72wjERdV468S8pGGwfrNDvxmB/qVuTThJJdo4teY0Go13KhxMDI6yuvXr+Mi1UFFEzg10USv09Hb28vC/MmGbsdLcs52hkkq2cTbtpZm3v31X8udmzd499d/LW0tzSe6JrVGTX19PZf6+zEajYyMjHD//n0ePnzI8PAwi4uLeDyFW5YLBAKB4Hh497vfzW/+5m8mP5YkCY/Hw8/93M/xLd/yLSXPJwQTgeANJhQKoVapTnsZguNClhkeHsbr9dHSsr/Vaz7Og8vksB1zEi4TRWMDgeUV6owmIrEY+tZ0wSaVpst9SZfJusvFjs9DT0MjETmGtwS3QCkuk3Qk9FeuYDabef36NVPT8dDLfKU5+2aQJBp6+rhlt9C+vJQUTSVJos1u5VZtFQ1dPVyxW+hTyhjUKnoqHVyqqog/xwFEEyjNbeLXW/HrrVlFE4VC4nq1HUmSePjgYTzx/hyKJmV2O4FgsOh8oaPgrGeYpJKrNEeSJNpbW7hz8zrtrS2nJ/pLElabjY7OTvr6++nu7sZut+Pz+ZiYmODBw4fcf/CAFy9eMDM7y9b29pkP3BUIBIKLwG/8xm/wD//wD/T29hIIBPiu7/oumpubWVxc5D/8h/9Q8nxCMBEI3mDUGs2JW8IFJ8fOzg4ajYa29jaqqqsLX7DLeXGZpHIYl4lVo0WFxOz0NCqFItk5xzeyv5wlwfbQCCvz89RW1bDq96KSFDk76JRCPpdJKsqeHrp1RmLRKC9fviSWIppkki/PBPa3G07Q0NXDP2+p5TutOv6/l7toc9iS5wqJJkcZBqtfX0gTTvwaE36NiWaNTHtHO0NDQ0xNTYHRDib7uRJNWltaisoXOioU5yDDJJViQmDPCpJCgdFkoqq6mta2Nvr6+ujr7aW2thY5FmNxYYEnT55w//59nj59ysTEBGtrawSD4m+wQCAQHCW1tbUMDAzwsY99jB/6oR/i2rVrfOITn+D58+dUVu6/cVcIIZgIBG8wSoUCvV7P9PR04cGC84ckxY8D8qa4TOqMRr7+8hU6tTquVVRSazBSe7kXyC6aNF3uw+/z0ykruVxTR4vFTrejImcHnXxkluWUSnt7OxarlZGRkWQeUabLJEGpookkSTR09XCn3EbXxsq+O/mBygYClQ2opsZPJdfErzFh821xo6aMSCTCw0cP8ft8SdFkn3ByBkWTiooK3G73sTkPZFlO+74plcq0NsPngfMkmuxDktDqdDjKy2lsaqKnt5e+/n5aWlrQaLWsr6/z6tVL7t+/z6NHjxgZGcHpdOL1ekVJj0AgEBwCvV7PBz/4QT75yU/yX/7Lf+FDH/pQWsecUhCCiUDwhnPlyhUC/sC5CAIUlIbJaCQYCOx1FSmBs+YyCdS2pLlMdBvOkl0m0sREVpeJJEnUm0x06QyUzy0mN5gJ0SQTv9/PjhSl7x230U7PUba4TKXBmLYxTQ2HzUWiLEeW5WTo7KI3nn2QrSwn02WSaDPc0tyMLMusra7mdZlA6aIJQLC2CQDV+FjW8wfNNYGjEU0AWnUSrS0tvHjxktnZ2dwlOkWKJnmFkyMUTSRJoqmpieXl5eLnKoFwOIxWq91bhkJB7BxuxM+1aJIFlVqNzWajrr6ezq4u+vr76ezswmq14vF4GB9/zYMHD3jw4AEvX75idnaO7W3XuXIHCQQCwUny53/+50UfpSLCCwSCNxxJktDrdYRCIXS73TIEFwNJoaC6upqpqSna8nRSyYdeo8Yfyp+xoFcr8Yf337XWK8G/+7DfYEfv22v/GTBXottJ35TqQ57kBljvd+1tknc2khtp2HWZpGzAdavzBDq70jb0mrkp6OtOCgS6S93pzovxcejsxNLbg3t4BHV3F46+XjaGhgmOjCVLY2ov9+J8OZyWOaLRaLDabDx//pyenh5WxieTAom1ryf5/6bLfXm/bgmcPi/P11aJxGKoFAquNdVTPpfe9k7d3ZXW2SeTrs5OXrx8SUVlJRJxl0lqORLERRP19ETW60ONrWjmppBGRveJThAXTbTOWVTjY/scPRAXTXSr86imxvcJWQFHLboNJ7rZ1wSaOvYet1ahc62gc06nuYj8Zgf6nQ30K3Npoplfb0Xvd6FfX0gX1jQm9CEP9oCLvr5eFp1OHj96zKXLl9BF/Mgrs0hVTXsLMpjBtxMXTXYFnTQ0Bgj5kF2rSNYc1l2VFiLBuGhisGYfk4lSBdEIst+DpN8TFWtra5l5+21qa2qQFEd8HyuLOCIlHj9ngd9+lOiJFvWalIuzLLgolApMZjOm1A4OskwgEMDj8TA3N4fP70OOxVCr1ZjNZsy7441GI8qj/tkRCASCc8S3f/u3p30sSdI+p17ixlaprk7x6ioQCNDpdIRCwdNehuAYqKurY3Nz60CW/4vgMlH3daeJBGkukywBsI6+7K6S1NIcpVJJV1cXra2tDA4O0nS5LymOlCqWAOyEQkRisWTo7E6JuUKeoVHUajUajQaXy5W3zTBkd5nA6TlNcnXQgdKdJvrtFerq6mhsauT58+csbMUdHQdxmgAn4jSRJIm6+npWVwuUAx0AjUZDIJj+2m6329kooc34WeIonCbFvK6dGSQJnV5PeUUFzS3N9O6W9LS2tWE0mvB4vUxPTfH48WPuP3jAo0ePGB4eZn5+nu3tbSKRSOHnEAgEggtALBZLHv/v//0/rl69yl/91V+xvb2Ny+Xir/7qr7h+/Tp//dd/XfLcQjARCN5wwpEIwVBIhL9eUBRKJfX1dUxMZHcWFMNZyTJJoHOtpDkVYH+WidzTHXeZ7FJsAGyC4MieKJArz8RqtWIymdjYiK8tIZyUIpYAmDUaVAoFi15PWuhsJtnCX+ncE4zsNhvu3VbDcnt71iyTfKU5cDSiSa5ck1LDYA/SdhhAv7WMwWCgr68P984OT0cnicVixyualIJyv7m3ISGYHHW5TJY7bG1tbTgXFw9UqncWSBVNzrJj5DhRqVRYrBaqq6tpaW2NCyl9fXR2dmG32wmGQszPz/Ps2TPuP3jAw4cPefXqFTOzs2xsbIq/9wKB4ELz4z/+4/zWb/0W73nPe7BYLJjNZt7znvfw67/+6/zYj/1YyfMJwUQgeMNZX1snEAhgsRZpKxecO6qrq/F4PMzPzRUenMFRu0z8BvuhXSapZLpMIp1daJ17G+NiA2AtvT37XCaFRJNQKITP5zt0m9Zag5FrFZV028uSobPanq4C7YXT8QyN4nA4koJJPrK1Gk6lGNEkWNuEanzs1HJNihFNJEmioaGBuro6HgyOEQgEj080KcVlAqBUpblMlEollZVVSfHtqEkVTVQqFU1N8faK5xU/ypLdJnqN+ny5Sw6AQhnv1FNZWUlTczPdPT309fXR3d1NZWUlcizG8vIyL1++5P5uRsrAixdMTk2xurqG2+1mZ2cHj8eDx+PB6/Xi8/nw+fz4/X4CgQCBYJBgMEgoFCIUDhMOh4lEIkSjUaLRaFyclGURWisQCE6NyclJrFn2NVarlZmZmZLnExkmAsEbjtfnxW6zoTmClqiCs4nT6SQWjaJWH+xurB8leg15cwP8khq9moJZJsnHiOBHlTfLxF9eH8+r0FvxVzXGMy12N88Jl0li0x1p7UQ3NZ7cqGudswR7utM2/erpCdjNMkmU5niGRpN5JhAvzSmUZ+IbGcXQ083Q0BBNTU04HA4ykWWZFZ+XnXAIs1pDVUYobCqSJFFnNIEx55c3P52dMD6OwWAgmFJ+kXCZZGaZJAgPje4TkxIUyjSBuHCiWZxh5ulzVquqcej1tJZZk5/nceeaFMo00W8t47dXYzSZ6O/rY2JunoYyE+bYDMqa5r3FHFWmSSl5JpAUTRJ5Js0tzTx+9Dj+83SE+SJajYZAIJDWHaCurhancxGf14vBeNAfvNMnNdcEcr9GvalOlASSQoHeYEBvMJD2aiXLBINBfD4fG5sbhENhQI533EoRPVL/zfrY7vjMjzO7NCFJKCQJjUaTLCNUazRoEv9Xq1GrNWg0apRKZc7XTIFAIMjHrVu3+PEf/3H+8A//kJqaGgCWl5f5yZ/8SW7fvl3yfEIwEQjeYBadTra3tujs7Cw8WHBu0el0KJXKZE17fUPDiT13QGNCF0rvDJItAFa/s4pfqcNvr0a/ld4xJBEAK8ug2lph3VJBuXstfu2uaJJ0LWQJgJUmxlgvs2F0LhMbesmcTkely4sqFtsTTSAZAJspmqSSKprIspxVLAFY8Xl5tbGWDHIFqDZmz3UpFffwSLKMSJZl5r0eXD4vtTs7xKLR/ZuULCQCYIsRTfIxYrDzldfzBBdXUVXXAI20OWzJ8ychmgAFRRNJoaCpuYmdnR2i29sYwuPoG1PWc1qiCSRFE7VKhc1uY9vlwmazlTRHPrRaHX6/P00wkSSJy5cv8/TZM/r7+o4+bPYESTpNUoSTXGMEGey2PdbqdNgLjz4S5FiMcCRCZNeZEg6H8Xq9uFwuIpFI/LFdx0rqq5gkSajU6qS40tTUhE4nguoFAsF+fu/3fo/3v//9NDU10dgYf78wNzdHZ2cnn//850ue7/z+hRQIBIdieWWFxYUFOjo6zl23BEFp2O12Kquq8Hg8B94YxV0m+e/Sxl0m+zcmAY0pLcskQb7SnESWSeomeDQQw+f1Mjg4iM/nR16aYXpmGp/PR3R+ghmtIW0+7eIMr6IR/IEAfr8PV6UDpVJJeXk5sWgMz7One4NTSnNScfT1ppXmwF55Tk0ex81OOB7kWpMIcg0fTWaAuju9Q82818PD1RVeBXy8tbLM6q4lPpVsWSZQuDQH4qJJrtIcgE1/AJ/JRofFSGR5iQ2/f9+Yk2o7rF9fSCvRSS3PSWA2m1FaHYSCIbbHX6ZnOZxGeU5GCGxbayuLCwtHmmWi02nxer1ZHtfR2tJyIHvyWSRRppPtEJwdJIUCjUaDwWjEYrXiKC+nqrqauvp6mpqbaWtvp7u7m76+PnpTjp6eHlqam6mqqkKn0/P8+fNDl0MKBIKLSXt7Oy9fvuQv/uIv+LEf+zH+5b/8l/zf//t/efXqFe0H6BopBBOB4A1lenqarq6uc31nUVAc8eDX+njpR13doeY6zQDY9fV1rFYrX9vdilNnQZIkWvRKVsuqUamULC0tEYvGUC1O8zISxeVyYbfZMd+5RbcKGhoa0Gm1VG1vUnbnBpIkIU28zts1J0Eu0SQzCDaBWR0Pcl1KBLmqj6fkzRUKEY7FaGxuxbu0zE40kiaAFuqYA7kDYBPkE03K9Do0SgWjaNEqFFSuLGcdVygMFvJ30EmllA462UQThUKBZLaj1erYmRxmybm0N8kpiyZarRZHeTlra2vFX18AnU6Hz+fLeq6mpgZZltne2sp6XiA4M+w6THR6PfYyO1VV1QwNDZ32qgQCwRlFkiS++Zu/mR/7sR/jox/9KO9+97v3uW8vXbrE/Px8wbnETkkgeFMpwrYvOP9sbmzw/NkzRoZHDtRaOJXTbjOs0WjZslSi3BWAaOtBp9NSXVWFVqPlVo2Dr25s4w/4qa2rw3j7Dq2KvflTA2DV0xPM6/eqUrN1zckXAgsQMhuQ5bhokimcVBmMXHJU0G6zc8lRQZVhf05ELrGlFKwaDWqFgjnPDmokTFk6sOSjGJcJ5BZNWm0W3tVcz63aSt555TL1nd0HCoMt1EHnMG2Hs4kmAGGtEbPFjMG3wbOnzwgFd90mBnP8KCCaFOSAoklbWxvLy8uH/n1NkE8wAejv72dufp5IOLdjSiA4a5RXlBOTZeYXFgoPFggEgizMzMwQLuJvnxBMBII3FJvNxvb29mkvQ3CcyDLT09P09fdT31BPV1dX4WuK4LRcJlcaqlh0xjt7ZGszrNPpuHf3LvorN6gO7nWL0Tpn04JLE6KJLMu8CnpxP33C06dPmZYj+F+8wO12oWltJhaLEhweJhKNZBVNFAoFFd3tWd0mkiRRbTTRYSuj2mjKKU4mrj0IiQBGu0ZLpV7PbaOJKpUq63PlKstJUMhlAtlFE0mSaLNbuVVbRZs9HvhaTOthOHyJzlGIJgGlDoNeT0+5iSdPnuB0pogkBUSTotoNH0A0UQR9tLe3MzU5eSStf9VqNcE8bWRVKhV9fX2MjY+f21bDgjeT5qYmFubn8WcpBRQIBIKjQggmAsEbSktLC8srK6e9DMExsrW1hUarRaPRYDabsR5BkORpukw0GjUNRnUy8FO/s5EWChpo6kCXssHVrc4T6dwTieSe7rQQ016Vgps3b2C12rhptdLW2haPjnj9mrHRMWYiYTweD66nz4D9ThOlUkkkEl97qmhSyDmSGHMYsQTi+SWP1lZZDfjZCgbjJUbsF0sKleUU6zKBwpkmCU5SNPGbHUWLJn6NaZ9o4lfqUKmU3GyuYmNjk6eZbpNTEE0qLUaqq6sZHBzE7SrcKjovklQwE8Vus9HS0sLY2JhoBys4N0gKBTa7HY/HU3iwQCAQHBAhmAgEbyg+nw9DStcEwcVjZnaW9ra2Y5n7NFwma1pbMuQvIZok0Ln2xD/dhjOtI0ukswutczb5cWppjmZ6Mtl21+hcpPLWNSxmC1cMBq5evUb93bvIMoRGRohEI9i6u5BlmeDwGJIksbDjZnRrk0Wvh5pLPfuEk8ThHR5h+uVLXg08ZyUcouZSerjsQUjml5jMhGMxXLvizUEL7YpxmcDRiiYHyTWB7GGw+pW5NOEkm2gC5BRNJKC7TE9tTU3cbbK4K5SckmhSW2bl1q1brK+vMTkxceiSmVgsvxBSXVVFdU0NU5OTRxo6KxAcJ2qVKj28WSAQCI4YIZgIBG8oCsXe3XHBxWN1ZQWlUoneUGTeQgkctcvEb7CnuUwC5sp9LhON38Xo6CgGgxG9f2/zmeoySZTmJIi0dqJb3QvzSi3N0cxNMafdy/tIiCbAvhDYRUnG7d5h+8kzng88ZyboZzrgI7y5yeD0FKNbmzxfW8Xpi3ciqb3cu++gvYlFh4UNh5VFhyU5NheZeSnZSMsvUSiwqlSAzEEkk1JcJrAnmhQSToK1TQRrmw6cawKH66BTqmgCUC776OvvY3Nzk6dPn8bdJqckmqijIa5evUpDQwNTU9MMDQ4yPjbGktPJjtvNxvo6C/PzvB4fZ3xsjECO0gStVksgECj4tI0NDZjNZubm5gqOFQjOAiq1WggmAoHgWBGCiUDwhmKzWfEHAkRziSayzOzMzJF2axCcDNFIhNnZWfr7+o71eQ7qMjlIm+FIJIrVZiVW15o8n+oyKbY0Jz3PRELd1416eiI+7lI30kT8/6miiUajpeHeXaxWK1eMZm7euMnNmzex9vcRkyQqt1zx1sF53rTvhOJthusSbYaLeIOfKAHKRYPRxJ3KKi6VObhTWUWDWoMc/7SyUijHBIp3mUBcNAH2iSayLDO55eKxc4XJLVfckXPAEp1cYbBHKZqkCicJ0cTgWaepqYnamto9t8kpiSay30N5eTk3b97g3r179F+6hNVqZWfHgyRJVFZV0dfXR3d3N7Nzc0xNTaWJ4bIs4/f7UamLCwRub29HkiSWnDk+V4HgDCEcJgKB4LgRgolA8AZjtVjY2dnZf0KWmZqeRq/Xs7q6mvOupeBssry8TFlZGQplYSfIQTmMyyRBKaU5CoWC1nJLfGx5fdJl4q9q3BcAm/x/SmlOZp4JQK9m709gQjQB9okmbbvdSjK751g1Wqw11TjDIVhdx6zJ3TrYrIm3GV5MtBnOM7YYdwnEA1cbTWYulTloNJmTYa/Z9JKi2guX6DKB7KLJ1LabL80s8Ni5ypdmFpjajmdwHHWuSSlth/OJJkBW0US3s4rJZKKvv4+NzU2ePXtGWKU7FdEkFZ1WS3V1Nd3dXTQ3N1PucKDT6TCZTNy6eZO62lpej48zNDTE9PQ001NT1NTUoFEXFjgT9Pb24vP5WBeCueCMoxYOE4FAcEB+53d+h6qqqoLjhGAiELzBBIJBjMaMdqeyzNTUFGaTifb2di5fusTk5KQIAjwvyDJLy8s0t7QUHnsEFHKZ+CV1TpdJ1vlyuEyCjhpi0fQOHqWU5iSIRqKEx4Z4GQkjsZdnIssy88ODDFc7mAkG4HV8Y27q6467NnbLc1JFkzqjkVsV1dzq7uGKwYhjdiGn2FFrMHKtopJuexnXKiqpzdJmOJVC7pLjpBSXCewXTTb9AULRGO1lVkLRGJv+QNJ18hY6JnZ8KMdGswonBw2DLabt8EFFE4WkoLmpiaqqah4/fkwwFMwtmsCxiCayv/hQy4qKCu7cucOd23dobm6moqKC5ubm4p+PuBh35coVPB4PU5OT+373BIKzQjgcRqUqrZ26QCC4+Hzxi1/kZ37mZ/jQhz7EBz/4wbQjwXd913ft3wdlQQgmAsEbjMlo3NeOLxKJEAwGadsNCzUajVRWVoo7jeeEbZcLjUaD8hjdJQmKcZkkOEwArD4aIOD3M7ztSwbA+svr964roTRnxmhEUkhcsxrQ37wBxEWThfo6vrzt4sXYGI9MemZDwaTTZCWRdTI+jizLbDfWM+z3MfPqFXVGI31lDi5dvUp5f7wEKjgytk84kSSJOqOJbnsZdXnaDAdHxvKKJQl3y3FxEJcJpIsmZXodGqWCiU0XGqWCMr0uzXXyN74YI7vf61yiyVGEwR6laAJgMZvp6+tjZGYRl9tNbH1x/xdCE88MOnLRBEoSTQAUCgmrxUJdXV3On7f81yu4fPky1dXVDA0N4hWdSARnkPX1dWpra097GQKB4Azx8z//83zzN38zX/ziF1lfX2drayvtKBUhmAgEbzBGkwmfz5f8WI7FCAQCBDPsrU1NTfEWxMJlcqYJBgJMTU7SUuLdZAC/38+O203AXzgYMu06lEW5TLJRKADW7/czsLiJ2+1maHiY5qZ4SUdCNAFKLs1pUUaYt1hQqlQYVxeSeSabwSDRinLa9Dqi62v4muqAeHmOLMsYd8tz5ocGebi6wpTVzFOfl5lXr9LW7+jrTWs/XGx5TSljEy6XXJzWb2lCNGlbcvKu5npu1VbyruZ6Wm2WrK6Tw+SawOmIJgqFgrbWVtzhGNvb20RWs4SjHodokpJnctJUV1dz8+ZNFhYWWFxYEH8HBGcHWcbr9WKxWE57JQKB4AzxqU99iv/xP/4HDx8+5POf/zx/9md/lnaUyoEFk6985St893d/N/fu3WNxMX6X5X/+z//JW2+9ddApBQLBCeNwONjZ2WF4aIihwUHGxsbYdrno7OhIG6dSqagoL2dzc/OUVirIRzQaZWx0lMHBQeobGjAXePMYCYeTYb6RcJiR4RFGhod5/fo109NTB1rDUbUZTj5GBL/fTzgcxmy28HU3LmMvK0t2zYE9l0kxpTkJ0USpVBIIBNLyTOSebqo2V9EoFcxYzGgUEnatNtk5p1upYMftxtTXjSsSIby6QqPJjLKyAnc0mtX1kU04yRRFMh9LvSYbxbhLVCoVwSK6oRwXocZWwk1ttC8vcdu1RZvdiiRJWV0ncPBck6MQTfx664FEEwCb1YasNeJ27xBemWUfF0w00Wq13Lx5E71ez9DwMLHdXB+B4DTZ8Xiw2WwHclAJBIKLSygU4h3veMeRzXcgweRP//RPec973oNer+f58+cEg0EAdnZ2+OVf/uUjW5xAIDhedFotN27c4O7du9y7d4/bt2/T29NDdXX1vrEtLS0sLS2Ju4tnEDkWw+V2c+PmTSorK/OO3drc5Pnz58zNzjI6MsLz58+x2W1cv3EDrU5XVPhVJkfRZjhbaY5Go0FGJmhJ/5z89uqSS3MSxDq66UjJW9U645vdVoOOdytj3Kyu5BtvXqNjPe5Q0V3qRqfTodoVkmq7O1EjMTs9iSccxlNVyXZjPaGR0bzCSeqRTSQpNrOkkLtEr9Pj8eZvWVwMpeaYZJKZa9Jqs+xznSQo1Hr4KDroZBNNgEOJJhqNBklvZmfHQ2hpZv8X4YKJJpIk0dzcTGtLCzOzWUQigeCEiZfj1J32MgQCwRnjQx/6EH/8x398ZPMdKCXpF3/xF/nUpz7F937v9/LZz342+fg73vEOfuEXfuHIFicQCM4OLpcLj8eDPxBAr9ef9nIEKajUauw2G0tOJzV5arkDfj/j4+Ncv34dtVrN7NwcnZ2dyW46fp8Pu92e8/pC6DVq/KFwzvPxAFjwh9PvTgc0JnSh/RtAu15FNBIfGzBXot9ZTW5cIV6ak9zc+l1x10BVI/qVOfxmB4HaFnTO6b0N9IZzz5mwOk+gswvV+Bha5yzB3h7aRkZpC+4QqqogvOhEPT1BuKUd8/XLrLz1AGniNY1t7XzD1Ss8fzXE1OYmq2oVW6tB7jQ1YJtbIDw6hrq7i3wcJNC1oLtkfBxTXzdV29s4nU7KHY6009LERNIxU/C5WtrTugYdlFBjK5q5KaSRUSSgraebNrs15/hgbRNa5yyq8bF9HY0SooluVzRJDfINOGrRbTjRzb5OimMBaxU61wraxSmGlGY2drw4zEb6DVL85yNFXEuIJmnim8aEPuRBv7WcdDX5lTr00QC6ndVkvo5KpSJqtOD17RBzTqOrzQhb1hgg5EN2rSJZc4iZKi1Egnm+khkoVRCNIPs9SPrs4cnHSVVVFZubm0y8fk1ra+uxduMSCHIiy3g9HqxWUY4jONuoDDpUel3hgaeMSnFxnFqBQID/9t/+G1/4whe4fPky6owucb/+679e0nwHcpiMjY3xtV/7tfset1gsbG9vH2RKgUBwxjGbzRiMRnRa7WkvRZCFxqYmFp1OZmdmiO7a5acmJ3n+7BnPnz3j8aPHrKysgCSh1mhAkmhqakpudra3tjAajUiKg1VqHlUA7NraGtMz0zycmMfj8XKzP33jnOiaU0xpToLM0pzhXb0mtdWw1jmbzDNJdM6BvXbDa3YT4XAYxeQkTWYzzV3tmJQKGt07hGMxXKEQlt4eLL09hEfHiiqfkWWZBY+Hoc0NFjyenJ2oEnMVcpcA2G02/D7/vhyi0yLU2Jq19XAujrJEJ2CtYmLDxT+8dZ+HE7N88dU4gz4Zv9mR02mS6jYp1mmiVChRGC34/X5Cy4cozzlAu+HTcJoA9PT0UF/fwNDQUPbW9ALBMbPj8WC1inIcgUCwn5cvX3L16lUUCgWDg4M8f/48eQwMDJQ834HeGdfU1DAxsf/u01tvvUVra+tBphQIBGccrVZLc1MTc3NZQg4Fp45Wq8VmtbK+vs7KcnyD53K5uHb9OteuX6eurhaPx8O1a9eyXr+wsEhNTc2BnjsajeJ2udjwBdGp8xsX85XmxGJRpqen0Gl1tLe3oa1pRqPeq59J3NVPzlWgNCdbnpMit44AAQAASURBVEkgGORapZVRczkPlzeZHx8h3BF3K+QTTRobG5nVxv9kShMT2DVajDXVzAWDKFZW0KeIHamth/MJJ4teL4/Xlhna2uDx2jKLWUppihJLxtPFg+qaahYWFnIMPh1SRZNCwslhSnQgXTRxYiAUidKniRGMRNjYiX+Nc4kmwIFEE4WkAL0Jj8dDdC3L1/4CiiaVlRXcunWLJecSs7OzxWfnyDLRSCR5iDJPwUHYWF+nrk50xxEIBPv50pe+lPP4u7/7u5LnO5Bg8kM/9EN89KMf5eHDh0iShNPp5I/+6I/42Mc+xoc//OGDTCkQCM4BdXV1BIMhfEeQkSA4esorKkCScC4tEQmHiaVsRGrr6ujr70ej0ey7bmN9nUDAj9VmK+n53C53UrGfm5vj9fhrXG4XAa+HjY2NvNdmc5nMuQLcvNxPTU0NBoNxb+xu1xzYLc2Jpm/MUkWT1K45yWtSyiQG/BHm/WG+OD7DV8NKvjC/ytSma18ILKSLJo71Vfz+AOq+DmKxKJXTU9g3N7EoJBqMBqpmZtOEi4TbBPaEk0zxxBUKEo7FaDCZd10qe2UZqeOLcZaY+vbKberq6tjc2CAaiwEk2yMXi3p6Ivl5HyVH6TYpVjRxmAwozDaGPBGMXhcO897PVUI0KdRBp1jRRKVUEVHrcLlcyAdtOQwHEk1OC41Gw40b16koL2dhYYHBV4PMTE/jdrmQd3/+UnG5XLx69YqpqSmmpqaYnJxieGSEoaEhRoaHmZ6eZmlpie3tbULBoBBTBNmRZTweD1Zr7hI/gUAgAOId3haz/E0ugQP9pf2pn/opXC4X73rXuwgEAnzt134tWq2Wj33sY/zoj/7ooRYkEAjSkWUZWQbFGagtlGWZSCSMSnW6b9IF+5manMTtdtPb04PL5eL58wGMRkNR1y6vxANO52ZnqaqqQldERs3c7Czr6+v09PSgN6Q/T8i1gdGUO1vBL6nRy/uzTjY2NqhvrkWlBP9u2YzfYEfv24p3zUn5k6WPBvArdXGXScpGFvbyTCDuMknkmWgXpgiHI6zaq4lNDdDR1MxrwLM4Cw4bkc4utONjBGubkHu6kUZG0cxNQV834aFRLmtVPH36DJVSSatCwXvtZcgdHcjA0ydP6AKk8XHo3MvXSBU73MMjaaKJIRRE4fMyDyjWNjB4A4RX17Nem5Pd7JJUJOIldBsbG1RWVAAUnV9yEqRmm8CeQJWNQrkmutV5VFPjOTNN2hrbgXY2PD4cJgN90R0kpycpovnNDvQ7G2m5Jn69Fb3flZZrUmymiV6nZzsYROF2Y5MkcGTcAS+UaZLIM/G5wFDkZlCpOrU8E4iHwVZXV1NdXY0sy7hcLpaXV5ifn0elUmGxWAiHw3i9XvR6Pbdu30aj3u80i0ajeH0+vB4PHo+XzY0NAoEAsiyjUCjR63XodDr0ej06vR6NWn3gEkLB+SYhlohyHIFAkI1YLMYv/uIv8mu/9mt4PPGbamazmZ/8yZ/kZ3/2Z1GU+LdDknMVTReBz+djeHiYWCxGb28vpjxvkM8Tbrcbq9WKa2lO9HYXnCrjr1+zsrxMLBbj+vUbmM2n+zsWjkR4cP8+jY2N2MvKTnUtghRkmfHx15hMRmrrDtYxIBaN4nQ6WVxcpLOzC7vdBhlvRiPhMEqVCkmSeP7sGdeuX886l39rDYPegNvrw2Q2k+strV4OpwXATk1P49repr+xmhnnCu3t7XtjfVtpgokuMwB2a3nPCbDrDvDrrUn3gN/sIDT6Ap1Wx7hs4B++ep9QNIbCYueb9dBhM+25FnZFE9hzQoQaWwkNjRCLysTa45vzwKvdDX97OwMDA3R0diJPp5R5pAgn2ZBlmZEXL3BHo9R2ddJgNJW2Adh1tGQKJgA+v5/Xr19zdVfMKkUwOS6HSTY0c/HuQ/lEkwSJjkaZwgnE82ggPQxWt+EE0rskQbw8C9KdR4nMm1RnUtKtlFLulXQzpWToJBxPqSVjS8tL1NrM8fdFmaIJQMgHkDsINhECW6xoEo27sE5LNMlFMBhka2sLrVaL0WjM6nArhkgkgs/nw+P14vV48Hq9hEIhYrsuFhlQKhRotFqsVit2m02E0V5gZqanaWhoOFRI+alT7O/2BSe553K5LtyeK/G5bf7/fhrLOQh9dfsDlH3sExfie/Hxj3+cT3/60/z8z/88X/M1X4Msy3z1q1/l3/27f8e/+Bf/gl/6pV8qab5DCSYXFSGYCM4Csizz+PHjZDDnxOvX3Llzp2RV9KiJRqMMj4ygVCiob2g41bW86fj9fpaWltjc2ECj1dLV2YlWd7g/yj6vl8nJSYKh0G4mykZ8A1JmZ3ZmBqVSiVqtwe/3cffevaxz7LjdGFUSL14NYjKZ6OjoyDou4TJJiCayLLPj8WBXRvB6vGjNtr2xvq342ByiScJlkiqaJF0mK3OEQmFeLK1zs8yILMsMBdV4Jsdw6LXUt3ehno6XcAQqG5IlINlEk0S73XDLnpgTeDWKz+dlzV5G4+7vhCe1LW8B4eRA5BFLEjx5+pSbFjP6y8V35TlJsSSVYoWTUkUT2O2OdAqiyeTUJF2Ndeh1ujdaNDkpIpEIfn+AtbVV1tbWALDZbJSVlR36dVFwhpBlBgcHuXfv3vl2mAjBBBCCyVniIgkmtbW1fOpTn+Jbv/Vb0x7/3//7f/PhD3+45BKdogWT7/iO7yh60s997nMlLeKsIQQTwVlgYmKCaDSabBO7urKCz+ejr68P5SnfOZNlmYePHtHUGN9YGI3GfW4EwfHz8MED6hsaqK2pOXJreiQcZnp6mrq6OkKhEEtLyzQ1NWIwGgkFg2xtbVNVXZV3Dj1RvvTlr3Dr1q2cP7OZLhOAaCxGeN2JzWZLlubAftEkkR+RTTRJdZkAqJ3TPJ9fpf9SPzrnNLDbenY36yLgqE3mYZQqmjiXnCgn5zBbzEgd6Rv5IxdOUnJS8oklAP6XLwg01FCbp9V0KomOQKchmMCeaAL5hZODiiaQ7jY5btFElmWGR4a50dMZb2mYQzTJKZiAEE0OQSQSYXVtjZXlZfx+P2azmbKyMsxms/h7dY7xeDxsbm7S39d32ks5HEIwAYRgcpa4SIKJTqfj5cuXdGa87xobG+Pq1av4/f6S5iv6HbbVak0eFouFL37xizx58iR5/unTp3zxi18UAUwCwREQi8VYXV1N61pSWVWFyWTi4cOHOduP5iMQCCTty4dFkiR6e3tZWV1lfn6ejc3NI5lXUBoajSYploSCwWQ74aNApVbT0dmJwWjEZrfT09uDwRgPzNRotQXFEoBoNMLVS/3s7Oywvb2NO0v7Ub+k3hcAGwgEGFvaHxrrN6Tbr5O5EUW0Gg7XttBhNyDLctoGObXdcGKTndluGPYHwar7ulFPTxAefsnq6hqOezdRSEqkiYm0kFVTX/eesDE+vq+jTUmkuEoKiSXSxAR6vZ7lleW84xKctlgCxYfC5uuiU2rbYSApoEG8dAs4kiBYSZLo6uzi0eBIPIB3V7RJQ2Mo3DkHig+CPeXOOWcJlUpFbU0N165d4969e9TX1+N2uxkaGmJ8bJz1tbV4lx7BuWJ9bZ26IkVggUDwZnLlyhU++clP7nv8k5/8JFeuXCl5vgOV5Pzrf/2v2dzc5FOf+lTyrmE0GuXDH/4wFouF//gf/2PJCzlLCIeJ4LTZ2NhgeXmZhsbGfeemJidpa2uL3yXLgyzLjI2N4/f7USqVuN0uJEmisrKSpqamA9eSZxIOh3ny5Al9/f1HMp+geCYnJggGg4TDEYLBABaLhe6eIoJCTxBVOMDAq0Ei0SjRaJQbWXJPMktzNre2WFle5lpbfPObz2UChUtzIL7pDU8Modfridj3NsrJTfPs6+Smulinidfrxf9iEIvVSiSjRAfi2SbZSHOdQH7nSQmOkgQJwUZ3qZunz55yqf9S3t/3syCWZHJYt8lZcpr4/X4mJye5e6UvXkJwUKdJKXekhdMkL36/n5WVFVZXV4nFYsnSnWICrwWnyEUpxwHhMNlFOEzODhfJYfIP//APvPe976WxsTH5evH2228zPz/PX/7lX/LOd76zpPkOJJhUVFTw1ltv0dWV/gZlbGyMd7zjHQXbSZ51hGAiOG1GRkawWCyYs/z8bW5sEI1GaWtry3m9LMu8ejWIVqfF4XAQi0bRaDQoFAoWFhaIxWL0H6HA8fjxY5qbm0Wd+AkTi0ZZXV3FYDBgNJl4/uwZN2/dOu1lpaEnrnY8evac2poaHA5H9nEppTlr6+vxDjzd3egSG9IDlubAXp7JxMQknZoYWq0Wv9mRVpoDpYsmD9ZdtLe1oV+Ib8Izc00S5BJOIIt4kkGxIkmCVLEE4NXgIM1NTTkF1rMolqRSTLZJoRKdsyCabG1vsb62zvWe3efMFE0K5ZnAgUQTIZgUJhKJsL6+zvLyMj6fD6PJhN1mQ6vVolar413hzvvm/ILg9cRb1h/l+5dTQwgmgBBMzhIXSTABcDqd/Of//J8ZHR1FlmV6e3v58Ic/XHSZcioH6g0aiUQYGRnZJ5iMjIwcmeVfIHiT2Xa5cnY7sdpsvB4fzyuYjI6Oolar0kp6EpSVlTE5NYXH48FkMiXLew5yt0aW5WQ5jhBLTh6FUkl1yvdYpVYTCgbRaLWnuKp0/CjRymGCgQBlOcSSBHq1En84it1uZ2ZmBoCAxpQUTZJz7rYaThAwV6LbWd3Xalgf8uDXmPCX16NfXyAajbBqKKch6o23G65tQeecRuda2RNNNpwEHLVEWjtRTY2jW50n0NmFanwMrXM2reVwjxr0VgtYLYSHRpPiQ7ilPSlYBF6NJkWMbMJJqYJILlLLgFI74iiVCiI5yg7OulgCJEt0NHlaEBdsPbwrfiWEk9S2wwnRJGCtQudaibuOjqHlsN1mx+PxMLe+TWO5bf8nWqjdMMTLc85Ru+HzgkqlSmuL7Ha7WVtbw72zQzAYJBIOIwPIMjKgUipRq9Vph0ajif9fo0GpUBybwBKLRolEIkQiEcKRCJFwOP5xNEp09/FIJIJKrabc4Yhvei6Q2LO+vk7dATvBCQSCN4va2tqSu+Hk4kCCyQ/8wA/wwQ9+kImJCe7evQvAgwcP+MQnPsEP/MAPHMnCBII3lVAohEqpzClgKJVKorEY0Vgs/sYsg8XFRYLBIK05BBWD0UhnZydDQ8MolUp0Oi3r6xs0NTXS2NhYdKDs69evWV9fR6fT5RVvBCdHZUUFU1NTdHV1HXkI7GF4OTbJrRvXyGdn9Etq9HI4LpoQz2fx+f0Y9HoCuxvSVJdJQjRJuEwSokny/K5okkqb3YRHpcRf3pjMqAikiiZNHehmXxctmvDgIZq5KUKNrUnRISGcJNwmxQonhyHTVZKKSqkiEt0vmJwHsSSVUGMrmrkppJHRnKIJgHbXDZQqnAQqG9CtzqOaGj920SSVTNGkvq6ewaFBKisr4i6XTJdJMaIJlCaagBBNSkCSpGReXzZkWSYSiRAKhQgGgwSDIUKhIF6vd/fjeJaUvCuuSMQFmUyBRa3RoJCkXdEjQiQaSRM8EiIIu3PsLg6lQpEu0KjVGIxGNBnzBwIBnE4nc3Pz6PU6HP9/9v48urX9Lu/HX1vz6EHyPM/j8XCOz7k3A8m3EDrQgbBSfjTwW4SGQkspLTTQFMo3JDSUUBqGUJoGaFrStUrTliYtHQjlFxJI0px7j499fDzP82x5kmzN2r8/ZOlonizZsu/ntZaSa2nvrS3bR9bn2c/7eaxWSktLi+rvQtbIMna7/W5XCQsEgoLx8uVLHjx4gEKh4OXLlym3HRwczOrYOY3kBAIBPvnJT/KpT32K3d1dAGpra/nxH/9xfvInf/LWGzyuixjJEdwm29vbXFxeUlNTk3SbzY0NqqurqaioiLrf4XDw8uUk/f19GX0w8ng8HNtsVFZVYTs6Yn9/n5raWtrb2tLu+/SNN+jry7yuVFB4PB4PY8+f097eTmVVigXXDSIHAoyOjvKu1x4hIeH0eFNuHxrNWVtfuwq1vQrrzGA0B1LnmXhXZjEY9Hgjgj1DIZ/JmnMg+XjO89HnPL4KwoVXbghvxJhN5JgORI/qwPXEk2SukkgWFxcpKS2huirooAkJJXB3xJJY0o3pZJNrUojxnMjRHIgez7m4uGBra4vHjx+D0y7qhu85sizj9XrDwkpIVHG73QRk+Uro0KDWqONED5VKlZecDofDwc7ODjabDY1GQ0VFBWVlZXdOPLlwODiy2Ri4D+M4IEZyrhAjOcXDXR/JUSgU7O3tUVVVhUKhQJKkhCUZkiRlXZKQk2ASyfn5OcCd/MYmQwgmgttkfHyc+vr6lOFzdruds9PTKMHC7/fzxhtv0NnZmft4jCwz+vw5D/r7MRgM2Gw2mpubEz7/9PQ0fXe91u8esra6hk6njRrVuU1Ojo9ZWFykt6eH6lITl25PykVAKAB2bmkFlUoVZb/WxbhMILs8k+XlZTq0MjqdLugSuHKZZCuaAKgW5jk7PUXXOwRSdBhsiNj64ViyFU8iBRJILpJE8mJigs7ODowG451zlaSimESTSMEE0osm6xvrlJeXU19+9flCiCaCG+Li8pLdnR0Oj45Qq1RYrVbKyy0olMUvnqyvrVFfX4/FYrntU8kPQjABhGBSTNx1wWR9fZ2mpiYkSWJ9fT3ltonWNqm4tmByHxGCieA22dvbY3l5mYaGBsqTfTCQZSYmJujr68NisXB6esrc3Bx1dXXJ98mQy8tLjm02Li+dOJ1OHj4cDi5wJQk5EGB7e5uNjQ0ePnqUt6YdQf7Y3AiKAIkalm4Lh8PB8tISXq+X/vYWdEZTWtHE6XSyuXdAY0P0YjSZaBLrMoF40eQcDVPT07yjsfJVVew1RJPLZ8+QJNg3lNHQ0IBiPug+SSSaQHLhJESsgJKITESSSJ6NjvJ2a1nY1n8fxJIQ6Zp0ilE0cZmrCMgBpqemefz4MRr/lfAhRBPBDeN0Otnd3eXg4AClUonVasVisaAoRpf4fWrHCSEEE0AIJsXEXRdMIvmzP/sz3vGOdwRDuyPw+Xz83//7f3n3u9+d1fFyEkxaW1tTvmGtrKwkfewuIAQTwW1zZLOxtbmZNIcEwON2s7Ozw8XFBUdHRzx+/ASjyZh0+1zYucpDiXybsFgsrK6u8jBBPazg9nHY7aysrOD1+RgZGQHA5/WiVCpv3YItBwJ47SeUlpalHc3h8pyN3X2aGhuZmZnBYDDQ0tKS1WgOxIsmz+ZXUas1DFUYcaUQTSKbcyC5aKKYncHpcjFz6ePBgwcoI2qAcxVO8oV/dgqfz4/JZLxXQkksqdwmIdEEooWTQosmiZpz4JVoYnfY2dvb49GjR3BpDz6Ya90wZCWaCMFEEIvL7WZvd5f9/X0kScJitWK1WFCqcoo6zDsXjguOjg4ZGBi47VPJH0IwAYRgUkzcJ8FEqVSyu7tLVcx4us1mo6qq6mZGcj71qU9Ffe31ehkfH+dLX/oS/+gf/SN++qd/OttDFhVCMBHcNm8+e0ZrayvaDNpO/D4fS0tLGI1GGhobb+Ds4MWLFwwNDt76AlyQmMuLC6anp3n48CEqtZpnb76JSqXC7/ej0+koKyujuroa9Q07hI5tNhYWFng80Mv84jKVlZVxf8xCbG1tUW8xM7+0ikKpxG63097eTllpaU55JhAUTZwaE3Ozc/SYVeivRnPgeqKJamEel9PFxLmTgYEBlCplwhEduBnhRL26hIzM6ekZ5a89Rq0ujkVPIcnFbXIboknkaM7q2ipVlVXU1NZcXzQRdcOCPOHxeNjb22Nvbw9ZlrFYLFitVlRq9a2d070bxwEhmFwhBJPi4T4JJgqFgv39fSorK6PuX1hY4PHjx+FIkUzJ60jOv/pX/4rR0VH+3b/7d/k65K0gBBPBbWKz2VhfX6ejszP9xrfE+NgYg0NDdz7g+b7i9/uDc+qHh/j9fkwmEz29vciBAHa7ndPTU05OTvF6PajUaro6OzEYDMHmpQL+TOVAgPPzcxRKJWaVxPLaetKGpb29Pcq1SpRKJUqdAZfLxeTLlzS3NFNdVZ1TngkERZNLlZHR56O8s6kapVKRN9HE7XLhcrtRdvShVqvCognECyeQf/EkMtB1XvZTWVFBdU31tY97l8jEbZJoRCdSNIGreukCiyaXxgqmpqd47bXXUKvVyUWTdKM5kJ1oIkZzBBni9XrD4onf76fcYqHCar1xsX1qcvJ+jeOAEEyuEIJJ8XAfBJP3ve99APz3//7f+Ut/6S9FXXj2+/28fPmS7u5uvvSlL2V13LwKJisrKwwPD2et2hQbQjAR3BSyLOPxeHE4HDgcduz24K27pydu7q5YcDmdzMzM8GhkhNmZWSQJenp7b/u0BEnweb0oFIrEc+myzMnJCWtra8gySFLwD4parcFisdDYVDjHkuS6wOV2cWq/wGqxxP2+Hx0dsba2xreMDOLyBoDglc+FxUV8Xi+v9bShUChyzjPZcXjY2NjkbQ1BcSQfoglAYPIlbo8HTferJod0wglEiyfh+9KIKJECCbzKJzk9PWNtbZWhoWHu4voi9L3IdYwoV9EE4t0m2YomyZpzILFosi8Ha92Hh4eCD+Qqmog8E0GB8fl87O/vs7u7h9fnpbysjIqKCjQZOGGvw+XFBYeH92wcB4RgcoUQTIqH+yCYfPCDHwTgc5/7HN/zPd+DPqJAQ6PR0NLSwg//8A/HtYymI6+CyS//8i/z6U9/mrW1tXwd8lYQgomgEDidTjweD36/H7fbzf7+PpeXTjQaNQaDAZ1Oj8GgR6/XF/Woy/LSEgaDgcrKSl5MTGAymfD7/ZyfndHW1kZ1ijpkQXEyPTWFxWKhtq4OZJmLi0umZ6Z57bXXCvq8ar+byZk5Tk9OePLkSdjdcm63Mzc3x8PhYdRqdbhqOMTJyQkrq6sMNNVgNBrwEC22ZCqaPFtYo7KighZVcPGYi2gC0cKJ2+Mh8HICc0kJ7rroFPZMhJMQiQSUWBIJCoFAgOfPn9Pf/wCDIXnTVjIiR1tiSXfO1yXWcRMrCEHmIkqqEZ1iE02mD+3U19VTWXVlHb4UdcOC4sbv93NwcMDO7i4et5uyK/Ek54a+FKyvr1NXW4vVas37sW8VIZgAQjApJu6DYBLi53/+5/mpn/opjMb8ZDvmJJg8fPgwyhYnyzJ7e3scHh7y6U9/mr/9t/92Xk7uthCCiSDf+P1+vva1r2G1WlEolKjUKsrLytHdgTfQWCYnJ5EDARQKJdYKK7U1NSwtLVFVVcXCwgI9PT2YzObbPk1BFsiBAPPz81xcXKBQKiktKeH4+JjHT54U/Ln1+Hk6+pz29nZMRiOXTidTk5MMDA6iv/rwHaoajhRNZOD09BSj7xIkUBtevVdnGgIryzJfm5ilr6+fCtfVPhmIJpDcbWIzWlhfX6evrw/VQrA15zrCSba8ePGCmppaajIYxUkkjiSr6YXCnrd3ei4rR022wkkxiyayDM/W93j99ddfOa2EaCK4I/gDAY4OD9nZ2cHpdFFWVkp9fX3eLvzcy3EcEILJFUIwKR7uk2CSb3ISTD72sY9FvXEpFAoqKyv5c3/uz9HTc/eT+IVgIsg3DoeDpeVl2toKe4X2RpBlTs/O2N3ZpaenO+pD0eXlJdNTU+GwUcHd4PzsnLW1VQaHhvD7/RwdHhKQZWprawv+3Hr8XFxeMj7xkvLycmw2Gz09PZhjRLdEokkI/8ke24fHNDZGZElkGALr9/v5xuQCg4ODlDkOg/tkUDkMSUQTWebrmweMPH6MBGHRBAovnMzPL6BUKujoSC48xIokqQSSVOTz3EPOkmxyXLIRT9KN6ORTNInNM4HMRBOv18vyuZeBwYixgxsUTYRgIsgHgUCA3d1d1tbXaW9rw3DNq7uXFxccHBwyOHjPxnFACCZXCMGkeLhvgsnv//7v85//839mY2MDj8cT9djY2FhWx8rrSM59QQgmgnxzeHTE0eEh9Q0N6Te+45wcH7O2tiZqh+8QG+vrSJKioJklqdDj5/zSydHhIUajMen7buxoTgif34/PtotCZwyGZ4a2z1A08Xp9PJ1dYnhoGPNZcFznOqKJ3W5Hq9USaHgVaJvMbRIiUoCA7EWI1dU17HY7AwMDUbkl+RJIkpGsDSgbMnGXJCNT4STbXJPbEE0uLi4IWBuwWK+aQERzjuCO4nK5ePnyJSUlpdTV1ZJrmNLG+jq193EcB4RgcoUQTIqH+ySY/MZv/AY/+7M/yw/8wA/wO7/zO3zwgx9keXmZZ8+e8ff+3t/jn/2zf5bV8XLyyymVSg4ODuLut9lsojVDIEiA0+kseDBasVBusVAsOqzX4+XZm28y+uxZ+DY1OcXZ6eltn1pRodPpODs7vbXnd6KkxKCntrY25R9pp6RGr47/G6NSKjEYDOBxRm9vKE94nFB2BAQXrWq1isbGRiYmJjgzBxeh4YVuaMTiauQitDgOL5avFtChOlpfWxd09+F0OsOLbgguxn1d3Wh31sML9Ejk3p7wDYIL/NhbImQZpqamqXee87jUiHYzep/I4+ZbLAmdd+h8bwNva0dYbEmV++JpasPT1IY0OxcnToVErEg3ULj96GrUCoKiWEgggwjR7EpEg6CwFhLZIEJ4O9p6dZ8mXphwKnUYDAbm5ubw+69EQcOVy+rqdysKjQH5LP5zWBiVFi7Pkj+eANnpyGp7gSAZOp2OJ0+eoFarmJ2dxRtzdTcjZJnz8/P7VSUsEAhuhE9/+tP89m//Nr/5m7+JRqPhwx/+MH/8x3/MP/gH/4Czs+z+NkKOgkmyxZDb7UZzw1VjAsFdwOl0onuLCCZAQWeN5UCA5aUlPG53wsd9Xi+LCwuMj40x8XKC+vp6Hj95wuPHjxkZGaGhsYHV1VXm5+aCq00BVVVVyLLMYQIh/CbRazIb40okmgRMFiQJFL7o3wunoRw9vvDXrpAgEiOatJQbaWtrZeLlBDZD8AN6rqKJXqdnr9TCheMC3cFmVsIJECdyhG6xAop6fRnX1Di9agmTyYTcF7/PTRB7frdBSDjxTs+lFU4g3tGTT9EEyEg0CYUPh4+l0jNUb2F+7tU5pBRNILVoApmLJsqgC0uIJoJ8IUkS7e3t9PT2Mjc/z/HxceY7yzLLy8vBLJT7ll0iEAgKzsbGBu94xzsA0Ov12O1Bx+b3f//38x//43/M+nhZ9Zb+xm/8BhB8E/w3/+bfYDK9ukri9/v5sz/7s3uRYSIQ5Bun0/mWukqiVquZnpqitbX12jPMIba3ttjc3GR4eJijoyNOT0+pq6+Py9k4PT3F6XTS19cXndgvSUiSRFlZGcPDw2xubvLs2ShqtQqfz0dZWRkdnVd2e1lmfX0dpVKJxWLBYDAgyzJ2uwOdXnf/hGFJorunh8mXL1lfX0ej1dLX2xuVQ3N0eMj29jZqtZqurq68Z9Q4UaLHj16jxunxJt9OCrbm6NXKuPEc2VyB8/QAtVGFQhEtqujxhUdzXOYqdPYD9H5XeDzHWV5DHXuoe3uZnpqiu6eHas85eucZTn0pzuom9PsbwZELsxVXXSu6nVV0Z/u4SqtfiSZXi+n2tnZevnyJVWek2RUUTiLrh0PjH9oUGSexRAogDoeD2ZlZ2rs6KbFYKAbpT+7tQZqdQ7OxUvBGnWSE2nVSVRN7mtrQbKwgzc5FfU/ddc1od9ZRLcyHfz6uqkZ0B5uoVhbC4zkh0ST0M3eVVqM72w8GA9e1Bl0mdhv6/Y2w2ObUl6J3nqE/2ooezznZixrN0ajV1KovOD05pay8LHinwRwcz7HtRI/naAzguUQ+O0g8nqPSBkdzLs8ys/8rVeD3ITsdYjxHkDdKS0p42+uvMzMzw8nxMa2trYlr7kPIMisrK5SVldHcnPo9USAQCBJRU1ODzWajubmZ5uZmnj59ytDQEKurqzm54LPKMGltDV5VW19fp6GhIWr8JtRt/E//6T/l9ddfz/pEigmRYSLIN2+88SY9vT1vmSslAb+fqakpampqqKpO39aRjs2NTY6PbdTX17O6uopWq6W1rY3V1VUGBweDG8kyi4uLnJ6e0d7RnpFA5XYFXQYKhYKJly9RXgXYOp1OFAoltbU1nJ6e4vV6Q09BTU3NrWV93AQBf4CZmWnUGg0lJSXoda9GBRoaG/H7fOG/ATUFCIXV408pmIS3S5Jnsru7S4UGVIaY0NgMm3MATmU1Ey8meO3118KugEwzTeCVaHJpqeH58+d0dnRSVlYWVT8cS6SzAVKLJ/v7+2xsbNLf3xccRSoyss01ySX0NRNC+SbJsk2yadDJJdMktjkHgq6ldHkmWp8Th8NBeecgCmWEEViEwAruOPv7+ywtLdHY2EhJSWn07zcExZLVVcwmE+3t7YkPcl8QGSaAyDApJu5ThskP/dAP0djYyEc/+lE+85nP8KEPfYh3vvOdjI6O8r73vY/PfvazWR0vp9DXb/3Wb+ULX/gC5eWJ58PvOkIwEeSbp0+f0tfff9uncWME/H5GR0d57fXXWVpcxOFwMDQ0hNfrDbozshGOZJk333yTx48fo1AqsdlsmE0m1Go1z58/D1ffXjgcjI+P87a3ve1a7of9vT02t7Z4ODyMUhVtwrOfn7Oyssrg4EDeKhOLkQuHg7OzczweN26PB4/bjQwMPHiApFBwsL/P8clJQRyFeoIiSK6iiQx4jrYxGox4peifXzaiydcm5nj8+DFIpBVNIHkYbEAO8PXVHQYHB9FpddHjHQmEE0gtniwvr+Cw2+nv70elzsokeqNIs3NZuUyuE/yainShsNcRTUIjWIUQTZTOcwIBGXN7X/QJX0c0ESGwgiLA7XaztrbG2dkZfr8frVaLyWzGbDJzeHiAwWBI2fR1bxCCCSAEk2LiPgkmgUCAQCCA6upz/H/+z/+Zr3/963R0dPAjP/IjWTvFRUtOAoRgIsg3bzXBRJZllhYXOT4+xlpRgcloDNZ6eb10dXZiNpvRX10Z97jdbG5uUVtbEzW+c3J8zMrqKhJQWlpGe0f81aYXL16g02pxOBxoNBqqqqqorq7OWczY2tzk8PCQ4eHhpMdYWlzEbrfHj/y8hZianKS1tRWjqTALqkxFk2RVwxMvJxjpbEalVOGMMaFkKpqcnp6hqawLu8JyEU0gKJx4vB4mDoMNNiEyEU4gQjyRwW4/R6FQYDSaQEo/whMiWVZKJmT6HInIRjQplGASIhO3SUg0kWWZldNz7JtrWLUamh8Nh38PblI08Z/ZMJmMqOsj3vtuqjnHH8z9EaKJoJDIsozT6eL09ISTkxMMBkPYzX7vEYIJIASTYuI+CSYbGxs0NjbGOftlWWZzc5OmpqYkeyYmY8HkQx/6EB//+McxGo186EMfSrntr/7qr2Z1EsWGEEwE+eatJpiE8Pt8YZeG3W7HeXnJ0tISarWa5uZmqqqrGXs+hrXCyvn5OS6ni76+XtbX1/H5/XR2dqLX65Me//TkBK/Xi8VqvXZD1/rVFa/BwcG0DpjTkxMWFxdpaW2lwmrl+PiYk5MTqqqqgu8Z93j0Sg4Eopw9hSKb0Rx4JZpcOp3Mzs4y8ugRutAiNIFo4oyJ8IoUTRwOB1rbLiUl5qhGk+uIJufn5+j1evw10QJEJsKJx+vh5cRLautqqa+rD+4X40JJR6RTIhuyGRWKJZvRnEILJpD5iM5STS1fWdvC4w9gcJzyntoKWkYehrfLVDSJrRuGiADhDOqGPR4PisszysvKkSJ/b3IVTXIYzQEhmggEBUEIJoAQTIqJ+ySYKJVKdnd3qaqK/ntos9moqqp61UaXIRn7ecfHx8Nz/GNjY2+ZLAaB4LoEAvK9XkCnInKkxWw2YzIasVZUIAcCTE5Osrm5SXl5eTjYbXt7m/WNDRwOB69lkIVUlqexQDkQ4PDwMGMRoKy8nEcjI0xNTrK2uorBaKTCamVtbQ2Px0NpaSlKpRKj0Uh1TU36A94hTk/Pwu6gQuJEiV6T3mUSCoENI8vB0N6NDZqbmtB5HOiV0aKJ01AeJ5pEBsH6dTrG9k/4FpMJvccRFk2cFQ3oj7aigmAhWjgJN+hECCeu5k5se7uU7W9SHlpcXy12Q4tu1cpCVJtOSDw5ODhgbX2drs5gDkqIXAWQbIl8HtXCfNitkolwkm0IrHp1qaCiSfjYSQJhQ2GwJ7PzeDR6OiylLAE2t4eOFEGwLmsdOttOfBDsVQgsEBcEmygE1qkxhUUTCGbD7Z8E0FxeEiVZJAuBvSLfIbACgUAgENwlZFlOqFU4HA50ObjDMxZMvvKVr4T/+6tf/WrWTyQQvFXx+byolMWbNXCTSAoFSgClkoePHsU9XllRgdvlQq1SBRe+NyQ0ORwOtFnWPiuVSoaGh6Puq66pQQ4EOD4+ZnNz89qul2Jk/2CfugKEvSYjXWsOXIkm6qDLxGAw0NjYyOnpKQAujSnsNInaJ4VoIikUKFUq5s5d9JXpU4omQFyDDhDXorO3t4+mqR29xYpufTEYGBqx2I0ME1WtLKDYXMbpdHIcUDE0OJj172chCAkG2QgnmYom6v6elHXA+SSySSeRaGK5mEZzdMQSoFEqMDc2gfM0ZXtONqJJiKSiSURzTnV1NVMzM4xotajrI75/N9icI1pzBAKBQHAXCE3BSJLERz7ykahgfL/fzxtvvMFwzGf3TMhp0P8Hf/AHw33GkVxcXPCDP/iDuRxSILi3eDwe1EUczlhMaLRa2trb6ezqujGxxO/zsb29jcVqTb9xBkgKBTqdDn8gkPWMZNEjyzjsdspvqCLbGZTX0GsyC/HVq4Pbn56dYo34ebo0JvRJtCs90VfQXeYqtF4nHo+H5qam8MI18sp/aHEbGquAiFGLiAVx2G1yts8Dqwm1Ohgy5mruxNXcGVxgXzlOZGQOjw6ZnJrkm8dnLChUKPsGGLYYKT07QHewGeVAuU18Xd2vapEzyEeJDVQtBkJuk0QiTUNPH99WUcZrHiff2tJAW1lJWBiKHFEKuYDC7UdXwkUo8DdEyG0EV6LJlSMJIsa7rsa9QoTChwHa29ux2+3I+zHf65gmqDCa4AdE+ewg8eOq7MU32RkvOgoEAoFAUEyMj48zPj6OLMtMTk6Gvx4fH2dubo6hoSF+93d/N+vj5hT6mmwu6OjoiJqaGny+u23hFBkmgnzhdru5uLhgd3eXxvu2eL7ryDILCwucn59TVl5OR3t73kSa1ZVgFkJrW+YtIcWGw+HA6/FSbnk19mQ/Pw9WOQ8N3ei5ZJNnIssyX3v6BiMjj1FE/DxT5ZkAcZkmvt1V9i/c1NW9unqvP9lLmWkCr8ZzZOBMY8LtduNxezAfbaNUKtj0KPAH/Pj9AeRAgIAcoMPnJCDLrPsUVFdXU1pSmtBKGpl3Ekmq0NhCExIQ0jlNMskzKVS9cDqSZZskatDJpT0nNgQWgsJaNiGwu7u7NJQZMRgMSNUx3+sUzTkiBFYgKFJEhgkgMkyKifuUYfLBD36QT33qU3l7HVkJJufn58iyTHl5OYuLi1RWVoYf8/v9/I//8T/46Z/+aXZ2dvJycreFEEwE+cDv9/O///f/BqCpqQlZlqmursFoMqbZU3ATuK7CQRONBl0bWebZs1H6H/RH2QHvCg67ndnZWSRJoq2tDY1Wi1qlYmVlhZqamhtzmESSqWgSuDgFGRSa+A8n2YgmTpcTpW0HdXll9LZXV/4jhRPd4RZer4epnSMuLy+RJIkHZg2SJLF85kKlVqFSqWnxX4IkIUlwIOlRKBRICgUajQaj0YB+/VUFrivRAjgJyYSUVORbZMlEOMlUNMlWMHFNJh/l0Q1k5m7Jh2gSOVaVTjTJujlHlpmemeFJbwdKpTJaNBEhsALB3UMIJoAQTIqJ+ySYxHJ+fs6f/Mmf0NPTQ09P9q7XrAQThUKRMuxVkiR+/ud/np/92Z/N+kSKCSGYCPLJ2dkZe3t7nJ2d0djYJASTImFvbw+H3U5HZ2f6jXPAfn7O3NxcsA2oqqoogn8Dfn+wl16desTl5cQEra2t6PV6xsfHCcgySoUCWZZ58tprN3S20WRaNfz8+XPeNtiHSqWKqxuG7ESTZ6OjvKu7if0LDw6HgxKzGZPZHCWarK+vc7B/wEh1CWq1Go/JEs6uSdSiA9EjGpFtOuHHI0Y6shFOMiUTgSUXQSVT0SSfgklILJE74veRlpaivk4nnty2aJKuOcfldLK6uspr/V2JXSYQL5p4LgHyKpoIwUQgyANCMAGEYFJM3CfB5Hu+53t497vfzY/92I/hdDoZGhpibW0NWZb5/Oc/z1//6389q+NlJZj86Z/+KbIs823f9m381//6X7FEXGXUaDQ0NzdH2ZfvKkIwERSCN998k+7ubiRFTtFBgjwzMz1NY2Mj5gL+G/f7/cxMT2MymW51PGdpaYkSs5m19XV0Ol2wOjkJDrudxcXFsPPG4/GwubFBe0cHAb8fxS0G2aYTTQKBAMsrKzgcDt4x1J9QMIHMRZMXEy8wGo006UCt1vByZQOvx0tZWSndZi2XTiebZ07a2lpRKJUJR3QgvXCSSDSB+CyMQogniYgVVLIRT9KJJulcJtmO5bgm5xKKJXHPGyGepBJOCiGaxNYNQ/aiSWg0Z3trC4PBQJO15HZEkyJwmVxeOjk5PUGlVKLRaMI3dRohWCAoKoRgAgjBpJi4T4JJTU0Nf/RHf8TQ0BC/93u/x0c/+lEmJib43Oc+x2//9m8zPj6e1fFyyjBZX1+nsbERxT1d+AnBRFAInj59Sl9//22fhuCK0WfPePz4MQCXl5fY7XYqKiqiqpDzgiwzNjbOo5ECjP5kyNbWFsvLy9TV1nJ6esrDR49QJXmdky9f0tzcTElpcX6YSzaac3R0xOrqKmVlZbR3dKCQJPSyN040ObIdIUkK6k1qQAqLJg6Hg/39fborzahUavbOHCwuLKLRaBgcGkRvPwTAqdCyf3DA2uoa39LegEqlTJtrAslFE0gvnEBxiCeZCif5EE0yEUxSuUuSERJOCiWaxOaZQGLRJDbPBDITTWRZZnp6mtf6ulAqFVnlmUAa0aSIR3MODw/Z29/Hfn6OVqvFXFKCHAjg8/nwer14PB78gQA11dXU19ej0Whu7NwEgpwQggkgBJNi4j4JJnq9noWFBRobG/nABz5AXV0dv/RLv8TGxgZ9fX04HNkFmee0MmhuDv6Bvry8ZGNjA4/HE/V4qquXAsFbFUmSbrQqV5CaqqoqRkdHUSqVaLRaJEni+PiY3r6+/D6RJBEI+JEDgRt1F8mBAKurq7jcbrweL0aDAYvFwuXlZVgs8bjdTE1NIcsyCoWC+vp6fD5f0YolEGzO0WteuUy8Xi8LCwv4fD6GHz4MVlJHoFcro0QTr8fL6uoqK2o1jzqb0KjVPJ+aJeD3U11dw4uNAzqtRowKBX19fRiNwRG6UOWwPuDGeXlJbV0t3sp6VCd7cbXDEC+chBfDCYSTcJtOCuEkcsEN8QIKFEZEiVr4h9pg0ggnvq7ucPVwItEkVDWcCvXqUkaiSTZiSeT2IbElkXCSrHbY09SGZmMFaXYuLJq465rR7qyHK4dj64ZDXLduGAjXDbe1tTG+sMLj3g7k/fVo0cRgjq8ahnDdcEqyqBoOiSY3gd3uYGl5mdaWFpqbmpL+DQ34Axwf23jx4gVIEvV1ddTU1NzLeneBQCAQJKexsZFvfvObWCwWvvSlL/H5z38egJOTE3S67MWrnBwmh4eHfPCDH+QP//APEz7u9ye2Qd8VhMNEkE9kWWb/4IC52VkePnwoRnKKlIA/wNjYc6qra6isrESXx6sBoUyQQo7/RCHLzM3Po1QqqautJRAIoNPpcDpdLC0tIcuBYBtLWRlra2sMDAzwYnwchUJB/4MHRb/ACI3mbO7ssrK6SmNDA7W1tYm3lYPCSkg08QeCP+fHj5+ws7NDpRYMegNeKUZoSdKeo7MfcHFxwbE7QMVV8HmiIFhI7zaB1I4TSO06idongYAChc1ASSecqBbmC+oyyXQcJxnp3Cb5dJrkuzlnc2OD0tJS6suCgt5thcDehMtkfn4eg8FAaVlZxvv4vF4ODw85Pj5Gp9PR0NhIhdWaModPILhRhMMEEA6TYuI+OUw+/elP8+M//uOYTCaam5sZGxtDoVDwL//lv+QLX/gCX/nKV7I6Xk4rt5/4iZ/g5OSEp0+fotfr+dKXvsTnPvc5Ojs7+YM/+INcDikQ3Fv8fj+zs7P09vYKsaSIUSgVGAwGjo4OeTn5Ep83fSNLpjQ1NbGyshJ0GBWYy4sLJiYmODs9pcJqxWgyYS4pQa3RUFJawqORRzx69Ijj42NmZmaoqKgAYPjhQwaHhopeLAGwewPYHXZKTQYeDg8nFUsAnFIw10CvDr4upUJBaWkZ+/v71NfVobHWoVKp0Me8bKchWKesJ/pKusscXGxWm7Svti2vwVleg97jCC9qIcJt4jwLj1lA0G0SdpxEOAvCz1HX+sp1crYfXlynwtXcGXeD4EI90e06hEWAK1Eg6XZd3WEhIZZIseG2kDs6kDs6cE3OJWza8bZ2BG/T0Y+FRJ5Il0ysMBQSk8Li0pV4ERK2QkJYpDgGMWKavjQsusErQU5/skdDQwPrGxt41AlauAzm4P8n+TnLZwcJ70d19Tt9eZb48UTHcmZna84Fm+04a9ebSq2mtq6O/gcPqK9v4GB/n29+85tMT09jt9sLdKYCgUAgKAZ+9Ed/lKdPn/Jv/+2/5etf/3o4RqStrY1f+IVfyPp4OTlMamtr+e///b/z2muvUVJSwujoKF1dXfzBH/wBv/zLv8zXv/71rE+kmBAOE0G+ef78Oc3NzWi02vQbC24Nt9uNUqFgd3eXgCyHxw/zwfTUFFarlZoUi/vrcnlxwdTUFD09PWi1WmZmZjCZzXQmaAKSAwFGR0d5+PBh2tacYuDYZsNxcYHf5+Po6Ij6+gba64OLzkzqhiOdJk6nk5nZWUYiKqWzac/Z3t6mRunBaDTiVEZfNcrWbQKp800gflGdqeskGfl0o6Rzm6TKM0nlMskk/PW6DpOoc8nAbZKp08TX1Y0sy6wcn+HYXsei09Dy8CGSJOWtOcdZXoPD4WB3ZzeYj+Q4uZd5JrIs8/TpU/ofPMjHwbA7HBzs7+Nyu6mrraW+vj5pnpNAUFCEwwR4izhMPvPxu+Mw+ZGP3MufRTJKSkp48eIFbWmKGXK63H1xcRGsyQQsFguHh8EwvIGBAcbGxnI5pEBwr2lra2NpaYmAP3DbpyJIgVarRaVWU1FRwfHxcV6P3dvby+bmZlzmUz45OTmhqqqKktJStDodw8PDSJLE3Fz81XNJoaC+voHllZWCnU++OD87Z2VlBYVCgV6vZ3j4IXX1dTjJ3A0T6TTR6/Vxj7tCV+8zcJrU19czvm3D7/ej97uit79qM4l0mkBytwkQ5TZJ5TiJdZ1k4jxJRCZulExJ5zYJjagkcpqkcpnEihOFJjbbJJZsnCbK+Tm+urLJv30+yX87uOCPN/dZPgoKb+mcJiHRLCyiXQlssU4T/ckeJpMJtUbN7u5u8DXsx3yPQ3kmsWgSuFJiydRloiys2CBJUv7GaCQJs9lMe0cHvT09eL1eno2OMjk5SQ7XDgUCgUBwx8n0vT8nwaS7u5v5+eBVo+HhYX7rt36L7e1tPvOZz6S0RgsEb1XKy8tpa2tjbn6OZ2++KT6cFTl6gwGFpGBrM/XIQTYolEqaW1p4OTGB7ego7+M5Ab+fw8NDampqwvdJCgXtbW3Yz+0Jn6+uvg6H3c7lxUVezyWfBPwBFhbmefDgAQ0NDdTU1qLWvHLEBENgM3PIRIomKpUKT8zYlUuT+Cp5ItGkq6uLby5tEwgEUoomsSM6kcJJ1D4xYzqJhBPIv3gSPu41xJNMRZNEyL09YafGbZPRiE4Gosmyw8mfPHvBqu2MU5ebTVnD2WJE21CM6yPWMRTrNEokmkDQzdTU1MTq6ipebfB3N040gaSiSd5Gc5Sqgo7mKBQK5EB+LzYolEqqqqvp7+9HrVaztp54dEwgEAgEgpwzTEJXND760Y/ypS99icbGRj71qU/xi7/4i3k9QYHgvlBaWobRYECpVInguTvAwMADjo+P8yqaVFVVMTAwwN7eHqOjo0y+fMnC/Dxrq2scHR4RyDYwW5Y5Oz1lZmaGsbExjEYjuhj3hKRQYK2wsrW9nfAQnZ1dLC4u3ki+Si5sbW1isVjiXlcs2YomRoOBs7P4BaFLY4pzmUC8aFJiLqG9vZ1vLGxyojKj97uihJNQrglk7zbJRDiBaPHEVdcaJZ5cR0BJJp6kIhPRJFmeSTLU/T3h4NWbJJXbJJVoEmLPbEGtlKgNeNm3X+AJ+LHoNNEVzda6qNGoUHNOCKfZGpdnEklo5Mt4dkBzczPT09NgKo9/Mfcgz0Sv1+N2uwtybIC6ujr2dndxOp3XOo7f7+fo6Ij9gwMODg45PDriyGbDZjvm+PiYc7udQKA432cFAoFAkJycMkxiuby8ZG5ujqampnCA4F1GZJgICsHp6Rlra6s0NzejFDPTdwKvx8Pk5CSPRkbyf3BZxuP1cnlxgdPl4uz0FIfDgUajoaamBovVGjdb73a52N3d4+QkOC7k9/vRarU0NjZSVp5gsXTF6ekpO9vb9PX3J3x88uVLamprqbxqfblxEtVtyzJ2u525uTmGhx9GuUoSEWrOySTPBEBy2lle30w6t5os0wSCuSahTBO7w87s7Cx9fX1UBILZEJnmmkDMqEWKfJPwNklyTuLOPyb3BK6XfRIeIUmTc5Iq0yRdnkmyLJNkOSb5zDBJRKpck2SZJnJvD8snZ/zJ6iYH+7t4/TLf9mSYP9fWiP4w+LPOtDknNs8EkjfnTJ9cUltbGxyXTpZnAvGZJncgz2R5ZQWVUkm5xZLX40ZyeXnJ7s4uDx8OZ7Wfz+dje3uHg4N9fH4/pSUlKJVKZFlG5sruLcvIsozP5+Py8hJJoaCkpASrxUJ5eTkajaYgr0lQ5IgME0BkmBQTb8UME7PZzMTERNoMk4xXbR/60IcyfvJf/dVfzXhbgeCtgt/vQ6lUCrHkDqHWaAgUynkhSWg0GjQaDWUQHmd0Xl6yubnJxuYmKpWK6qoqlEolOzs7BAIBKisrg9kkGTYuyYEAZ6enSFLy7fv6+hgbG6OstBT1LXx4Hx8fR6vVhpukzs/OWFhYQKPREAgEUKvT/5txokSPH71GnZFocuL201RXk/Rxl8aEzuNAr4wXTZyG8nAYLCYzAw8GmJyaDIsmer8rSjQJO00SCCdht8nRVnAxHOskiFws72+8WkSnEU4i62ohKKBEuk6yFU/Ci/o0womvrQvVygK6g8040cTX1R0WTRKh2VhJKJqoV5cSiia6gZ6CiiZyRwfS0hKuybk40cTb2gHTc1GiiaepDc3sHG093dDayHFNJTX2Y9p9l/glCVdVI7qDTVQrC/jauoIuE9sOuvXFoKOntDroDtpZxVXXGnSZ2G3o9zeifw+OtsK/N06NCb3HQcuVy8RisaAiOJoTJZoYzK9Ek0g0hrBokpTLs8wWd0pVWDTJJ8ncYPnEYDDgdrvSbxhBICAzNjZGeXk57e3tGYdny4EAjosLTk5O2NjYwOv1YbVaaG1tFeKJQCAQ3CCZOv4zXrmNj4/n9YkFgrcSx8fHzM7O0tramn5jQdFwbLNlPyZzTfQGA13dwcwHt9vN1uYmPp+Pnp5edFleobCfnzM/P4/ZbKarK74pJ4RSpaK5uZmZmVlqaoIL6crKKhTKm6nBViqVqFRqJicnaWpqYnFxkaGhITRaLaPPniHLcvBviyxzeXmJwWhMeJxsRBO9Xk/AaUevVuL0Jv4ZZyKa6PGBXs/AgwGmpqYYGh6ixHUaJ5pAUDjRn+wFW05i3CaRwgkkdpuER3UihBPIzHWSSECJejxDAcXV3IlufRGdbSdn0US7MB/nMpF7e6JyQEKo+3viRmBuklSiCQQdMLFOE8XcPO29PbSXlwLVSBGjSJmKJiFiRROnvjQ4yhUhmgAYzw9pbGpiZmaGwcFBcJzEiyYQHM1J8HOTzw4Su0xU2qDLJFPRhOBoTj5dJkajkf2DJKNDeSQbaVyWZaamprBYreEShEyRFArMZjNmszl0ME5OTng+NobJaKSjoyNhMLVAIBAI8kumgzYZCyZf+cpXcj4ZgeCtjsvloqq6GvNbxOJ2X1AoFOgNGTRKFAitVkv7Na6eb29v09jYSHVNcidFiKrqanw+Pw7HBWdnpygUCiqzXAjkisViwefzYTAaWFxcZPBKLIHg4iIQCOB2Opmfn8fr9dLS0kJVdeIFfkg0SYdKrWZ8co13jQzlTTRpbWtlamqa4eEhFJICvT24yMvUbQJB4STkNoHUwglk5zqJel0RAkqk+yQT4SQb0SQZ2p31hKM5yVwmt0ky0cTb2hF0v0SIJp6mNjQbK0izc+EWIHddM9qF+XD4bUg0CRESTcJfX+WZhH5GIdEkREg0CX995TKplV0c+f3YbDasVis4TqJfSMhlEiuaXLlM0oommXDlMsmnaGIwGLhwFC5UNoQkSQQCARQZuPdWV1dRKKSsxZIkT0y5xUK5xYL9/JzJqSlUSiUNDQ2UlZejuQO17wKBQHAX+cM//EPq6+vTbpeXDJP7hsgwEeSb4+Njdnd3aWxqSr+xoHiQZUZHRxkZGcl4BKaYODo8ZHl5GZfLhdVawYOBBxntd+FwMDMzi8lkpL6+gZISc3zGyDXxeDzYz885PT3l+PiY7u4eSkrj328XFxaw2+0EZJnuri78fj/7+/t096SundXjx+nx4na7GR0dfWV1j3gdpSUldHV1oZeDbpRkogmkzzQBcKJibX0Np9NF79X56RKIJuH9UmSbQPp8k7jtI0NCsxBPIol0nmQinqQb0VGtLCTNM8k0yyTkMEk0lpPLSI4jxrFiyrDCOFmmSSiYNtJpEmr+CYkmocDbyMYg3cFm3vNMHOYKZmZmeP1tb0OpVCbPM0n088pznkk+XSbT09OUlpYlfI/IFwvz8wwODqYdiwkEAjx9+pQHDx7k/X0xhNPp5Pj4GIfdju8qq8pSXk55eTlmcwkKhXBz33lEhgkgMkyKifuUYZIsSkSSJHQ6HR0dHbz3ve/FkmE2lhBMEiAEE0G+8fl8PH8+Rm9f722fiiBLXoyP8+DBg4zn04uN2ZkZrCHbeJYf7k+Oj1lZXaWutpbautRhn5kQ8Ac4ONhne2cHieCVY4vFQkVFBQplgmqaK87PzsMLJTkQYGJiguGHD9M+nx4/l24Po6OjPHnyJPW2eRRNxsbH6O7qxng1OhQSTeDmhRPITTzJRjhJJZokC4FNFgAbGstJJJokE0yAjEUTx/QcdHVF37nwygmTTjzJp2gScpnkKpqEXUgxosmOrMHucNDf3x92mWQjmqQUTCCzhV6eA2AvL51MTU/Rk0YovQ7T09O87fXX046WHxwccHR0RENjvBBYKEICs91u5+LyEgkoKSmhvNyCxVKO9sqRJ7hDCMEEEIJJMXGfBJNv/dZvZWxsDL/fT3d3N7Iss7i4iFKppKenh/n5eSRJ4utf/zp9fX1pj3f3LpkKBHcQlUpFIHCzWRiCPCDLeDyeOyuWQDAHpbKyMqcroeUWC48ePmRra4u52VnGno8xPjbG4sICB/v7wRrONJq7x+3mzTfeYOz5c8bGnnN+fs7Q4CCPRkbo6e2lqro6pVgCRF1VlhQKfD4fciCQ0WvQa9XIsozXmzrTJFQ3rFcnPxfXlaCRrnb4tYFeVlcjBAdzFS5zcBEaWT0c3jeifji2ghiCC+JUVcRx219VE8fWE6eqKI4lVFUMpK0ojqwfjiVZ3XCk0yKSkLiQiEQVw4lyRZKRUCyB4H1X9zum5+IcKFHnl6RyOCTmROatxIo+IXEoJBaFRKSwqHQlYIQFqCuhKiRexQpfIfEsUlADqJM8uFwuTk5OwlXD8n6CSudcq4YzQRmc+M5X1bDBoEepUOC8TBNQew0kMsvh29nZufFGSI1Gg7WigpbWVvr7++nt66O8vJzz8zOePXtW8FBcgUAguEu8973v5du//dvZ2dnh+fPnjI2Nsb29zZ//83+e7/3e72V7e5t3v/vd/MN/+A8zOp4QTASCG0Kj0eBLs2gTFBmShNVawezM7G2fSc4EAvK1xokkhYL+Bw+oq6vj0aOHDA8PY7FaOTs7Y35+ntHRUcbHx1lcWGBleZmV5WWWl5ZYWlpiaXGRubk5KioqeDQywuMnT+jq7r62AGUymTg8PEy73ZbtlLPTM4YHH7C+sZF2+3yIJk5DOWqVms7GWi6d0Yu7dKJJpsKJs6IhLJzkKp5kSqRokko4yUQ0ibu/qzvsukiELMusnJ4zun/IZn1dxuFsORMjnCQ9ryxFk8gw21hHTazzJtalE+vucZqt0aNXsa1KV7+fA1Yzs7OzBAKBsGgSheEqcDT256UJZjalFE0uM1ycK/PbCNfb28vKykpakbaQyLLM5aUT3S2HskqShNFkoraujp6eHmZnZwv/70MgEAjuCP/iX/wLPv7xj0c5ZUpKSvjYxz7GL//yL2MwGPi5n/s5nj9/ntHxhGAiENwQZWVl2O0Jah0FRU17e1vYDn2XcLvdLC4u5uVYBoOBktJSkCQkhQKr1UpnVxfDw8M8fvKEocFBLBYLJpMJs9lMaWkp5WVllFssNDY2pu23z5b29g42t7YSLpwC/gCHBwe8GB9ne2sLhSHYRtFYmz74Fq4vmkBQOFGr1RgV8ecXcpvo/a6MhJOk55ml6wSixZNsXCeZuk1CokkyYl0mIWJFE7m3B83GCqtndr66tc3o3gFf3dpm1ZVh8GgCkrpLEpEH0SSWWNEksl7ZVdUYF5AbcpmEv45pNYodu4oa29KYUCgkamtrWZi/eh5TebzLJCSaxKLJIOg6U9GEfLpMDFRWVbG3n9rxlAvBJq70H4mPj4+DmU5FhEarxWwuYW8v/98XgUAguIucnZ1xkKBd7fDwkPOrz/NlZWV4PJ6MjicEE4HghigrK8dxA0n/gjwjSbS0tkSNWNwFVldW0KjVPBp5VPDnUiiVWCsqqKquprKqiorKSqwVFVitVsotlrwHI6o1ajRqNaenp8iBAA67nZ3tHaanpxkbe47NZqOru5vBoSHUGg1uSR2sLs4wKDEfosmhX4Xf7w826CTaP4XbBF4JJ8ncJuHtchBOgJxcJ5mKJvkYzQE4drvx+AO0l5Xi8Qc48XoTjuXAq2yRvHHlNkk1opNKNEk0mhNbmxwpmkD2ozkh0STRaI5TY6JR6cPucESNa+R1NCcT0STPozltrW0cHR5l/CE3U7xeL1pt6rBXuJ1xnEywWi3YjjN3jgkEAsF95r3vfS8/+IM/yBe/+EW2trbY3t7mi1/8In/rb/0tvuu7vguAN998k64ML6QIwUQguCFKS0u4uLi47dMQ5IDyKjfjLqHV6e51EGBLSwtzc/M8f/6clZUVXG4XTU1NPH7yhJ7eXgwxddAOn4zTeYlek9k40HVFE61Oy/zRebB6GF9C4SSd2wTS55uEt7sh4STkNkk3opPNaA4kdplUHx+gUSpYPj1Do1RQ1ZtYXMkmxyQRsiyz4bAzeWxjw2GPHm1I4zZJJppA4jyTkGiSa55JiGR5JrEMVZYyMzMTfE35Hs3JlDyO5igUEv39fSwtLeV1NMfjdqPVpQ9ldFxcxL23FAN6vf5GqpcFAoHgLvBbv/VbvOc97+H9738/zc3NNDU18f73v5/3vOc9fOYznwGgp6eHf/Nv/k1GxxOCiUBwQ6hUwSvOgrvH/Pw8Xd3Jr4QXHbKMBPdaoNvf36e+vo7HT54wODREW1sbZnNyq3xZWRkTc0EXQr5FE5fGhF4ZLZwc7B9QXh5coEYGwiY8RoZuE8hcOMkm5wRyE04gsdskVZ4JZO4yaTPo+HMN9TyuqeLPNdTTWlqYUYjNCwdvHOwzeWzjjYN9Ni9ivr85iCbZhMCGyCTPJHI0J1GeSaLRnKqqqqDAAPdiNKe0tJTamlrW15Pn32SLx+NBl0ZglmU5KNIUqEr4OkgKRTCvRiAQCASYTCZ+53d+B5vNxvj4OGNjY9hsNn77t3873GA4PDzM8PBwRscTgolAcIOI4Ne7SWNjI/Nzcxk3s9w2LpeLvb19rNbs62SLncvLS6YmJzk/P6exqSnzHSUJjUbDmTsoWuRTNIF4t4ndfo7V8ur7n4lokonbJFPhBK6XcwIURDTJxmUiSRI950c8rq6krawESZJQ9/dcaywnEAjwxsEe/21thTcO9ggEApx5PHj8fvQqFRsOOwtnp/EBmhmKJpEkEk3g5vNMmtUBTo6PcURkaGU8mqMxFOVoTnNzE/5AgGNbfsZQPB4PujQOE7fHg0aTfmzntlCrNbjcuef8CAQCwX3DZDIxODjI0NAQJlPuNfdCMBEIbhBzSQkXBaxFFBSGqupqGhoaGRsfvxOiSUCW0ev1lJUnsODfda4qgh+NjGS9a2lpKUdHRzgJqhqFEk00+FEqlahj2oBCLTrJRnQg2m1SDMJJOrdJJqJJLL62roxcJqkqhmPJdCzn2dEBf7C2yjf2dvmDtVWeHR1QqtFw4fPxxsEehy4nK+fn8S4TyCgMNl0IbL7yTEJkWjU8XF3G1NR00CGRzWjOFcU2mgMw8OABOzu7uJzOax/L6/WmHclxOBzob7kdJxUGgz5KFBMIBIK3KhcXF3zkIx/hHe94Bx0dHbS1tUXdskUIJgLBDVJSUsLlPR6TuM9U11RTVRlhbS9mZLkYXeN5wWA04g8EchKudDodTmdQhAiJJpnilNQ4JXVGoonD4eDJ0IPkx8rQbQLJx3Tg5oQTSO02CYkmych0NAfiXSYAmo2VuPuSuUzSsXt5iScQoLOsDE8gwO7lJY1GE23mEir1el6vqsGkVnGWLFQ0hWiSbZ5JiGR5JiFyGc2JJDiao+CB1RSs5YV7MZqjVCoZHh5mcWmJwDXHXT0eb9qRHIfdXtSCiXRf3/QFAoEgS37oh36Iz372s7zrXe/ix37sx/jxH//xqFu2CMFEILhBSsxmLoXD5M5itVrCdWSC28NSXs5+DtWiGo0Gr/fVQtiJMmOXSXifK9EkmXBycHDA0uEZKpU6aYMORIsm1xnTgcILJ5mKJtcdzcnUZaLuzzHkdWGBWoMBjULB4ukpGoWCWoMBSZLoKiujyWjG6fOhVigpTTV6kaVokizPJHY0J5JCjObodFoODg+jRPu8juZkQp5dJgaDns6OTpaWlq8VAuvxeNKGZHu9XlTq7N4vbpJAIIBCIT7WCwQCwR/+4R/yX/7Lf+Gf//N/zk/8xE/cbcHkX//rf83g4CAlJSWUlJTw9re/nT/8wz8MP/6FL3yBv/gX/yIVFRVIksSLFy/SHvN3fud3eNe73kV5eTnl5eV8+7d/O2+++WYBX4VAkDl6vV7MGN9hFhYWaGhouO3TSIskSfEZDPeIqqoq9veTLN5SoFar8V5lCIUcKrmKJhA/oiMD6+vrdHR0JA2DjTrO1YgOJHebQGZjOnB94STlthnmmmQzmgOZu0wyQTfQA4uLrNvtvLAdsW5/1XhjuhJZnlRU8Z0trbyzppbvbGnlSUXwe9toNPF6VTUDFiuvV1XTaHw165ywRScD0SSS2NGcEPmuGg6RbDTnYXU5k1NTwV/WfI/mZOoyUary5jIBqKqqpKTEzO7ubs7HCASCY3SpKPYMsoAso1Bk55wTCASC+0h5eTkWiyVvx7tVwaShoYFf+qVfYnR0lNHRUb7t276N9773vUxPTwPB+aN3vvOd/NIv/VLGx/zqV7/K937v9/KVr3yFb37zmzQ1NfEX/sJfYHt7u1AvQyDIGEmSkCCvdYiCm6O5uYXNzU02N+IXeZkiBwIFnzNXKJUE7vHvmCzLuN3JhYNkqDUaLi4ueD46yhtvvMnuTnBRmC/RZGVlhcqqKrQR7oRU1cPhY2XhNoHUYzoQL5ykI7ZVJ+l2EbkmCc8xRZ4JXM9lkulYzrrHzdf395iw2fj6/h4bMVWrCoWC16tq+K6WNl6vqglfkZckiSaTmQGLlSaTOWq8IWmLThrRJFGeSS5VwyESjeZEkslojlKpoKy0lPX1teCd92A0B6Czs5Pz83PsOTgAfV5vXN5QIjQaDd4irpeXAwEUSuEwEQgEgo9//OP83M/9XN5c/bf6zvrX/tpf4y//5b9MV1cXXV1d/LN/9s8wmUw8ffoUgO///u/n537u5/j2b//2jI/5H/7Df+BHf/RHGR4epqenh9/5nd8hEAjw5S9/uVAvQyDICp1OhyfZfLygqCm3lNPW1sbl5QVzs7NZ72+z2Xg2Osry8goTLyYKVjOtVCjuRDhttvj9fhYXFlhcXGRgYCDr/TUaDW1tbQwNDTEyMsL2zqsF/HVFE6/Py/GxjabGxrjtMhVNMnWbZDKmA6+Ek0zcJkBWbpNsRZN8u0ySjeWc+nz4AgFazGZ8gQAnnus7+s48HryBAE0mM96rVp0waYJgc80zCe9fgNGcdr2Snd3osNSMR3MozgBYSZIYHh5mbW0Nb5Z/X4+Pj6mqrk67nVqjwVfEgkkgEEApRnIEAoGAX/mVX+GP/uiPqK6uZmBggEePHkXdsqVo3ln9fj+f//znubi44O1vf3vejnt5eYnX601py3G73Zyfn0fdBIJCUVJSgsORvytrgpvFYrXS3d1NIBBgfW0tq30P9vdpaWlhaHiI6uoqpiYnC3KOSqWSwD0STORAgO2tLcaeP0er0/FoZAS9IYOr3TFIkkRVdTUqtRq1Ro1CUkSJVtcRTfB6aG1pTZohECmaZOo2SUWmYzpATmM6mbhN0okmcfc3d+bsMoHE4a+xlKlUqG021ux2VAoF5ZosFvFJKNVoUCsUbDjsqBWK+HyTrsRVyZnmmUDhW3Oinwwe1ZQzOTkV/Dqb0Zwrl0leRnPIr8tErVYzMDDA3Px8VqMzJycnVFdVpd1Oo1bfgZGcovlYLxAIBLfGd33Xd/GTP/mT/NRP/RTf/d3fzXvf+96oW7bkV+LPgcnJSd7+9rfjcrkwmUx88YtfpK+vL2/H/+mf/mnq6+tTulQ+8YlP8PM///N5e06BIBUVFRWsrq5isVrTbywoTiSJ3t5eXkxMoDcYqMrgw/b21hYej4fKigoAamprOTw6wmazYc3z74J0Tz40y4EAOzs77OzsUFZWxsjICIo0OQPZYLGUs7+3R119ffi+oGgCTk/mC6OdUwdGBTTUVuPyJncNhUQTnceBXgnOJJuGRZPLk6tzSvynOiSa6OwH6P0unMrktahh0eRkLyyaODWm5NtXNKA/2kLvPEu86OZKNNnfiFukh9Cd7cct6CE4mhM7XqI72IwbQYlE7u2JExXU/T0wPReVD9I9MgTPJzixWinXaGkyJX+NmdJoNEFV0GlSqtFE5ZuE6erCMT0XzkoJn3dHB1JMs5a3tSNqnMjT1IZmYwVpdg65twd3XTPanXVUC/P4urpxVTVGuXBc1roo4SnUmhMSqkKjOaHcGae+FP3RVthB5NSY0HscGE1GtjY3aWhsDI/mSNURDheDGS4TjA9qDOBJY3O+PAND4t+bMEoV+H3ITgeS/vo/JwhekOjr62N2Zobe3l6UqtQfc+VAAJ/fnzbwFe7ISM49ee8XCASC6/DRj340r8e79XfW7u5uXrx4wdOnT/m7f/fv8gM/8APMzMzk5di//Mu/zH/8j/+RL3zhC+h0yT9I/szP/AxnZ2fh2+Zm7vkEAkEqZFlGlmUuRLXwnUdSKBh48ICNjQ1sR0cpt93d2eHg4ICBgYEoMaOrs5PVlZXCZNrc8YrJ3Z0dRkefc3l5ycjICJ1dXXkVSwCsVitHtniXRDZOk0AgEKw11RqRiA+CTUQmIzqQndsk2zEdSJ9vkumITiKnSTajOclcJrmEv0qSRItWx7C1gmZzdBaJqb8HFhZS7J38mMnyTWLJNM8Ebn80p9OoZnNrC3dEEPldH80BKC8ro6e3l9nZOfxpBI6zszOsGQYDBlu2ithhIgQTgUAgKAi3/s6q0Wjo6Ojg8ePHfOITn2BoaIhPfepT1z7uJz/5SX7xF3+R//N//g+Dg4Mpt9VqteGmntBNICgEXq+X0dFR/H7/vcyYeKuhUqsZGhxkbW2Nw4PEi4f1tTV2d/cYHByMW/BrdTqqq6uZzSEPJRX7e/vcZblkfX2dw8NDRkYeFUQoCWEym/Ekaa3KVDSZnZujob4enU6HU1KnrR0Okc2IjtNQnjIQNnzMAozppBNNUrXnJBvNgfgAWEicZRJJNuGvsa6OGyGLPJNiGM2RpGBrzosXL4IjfKlGc2Ip4gBYCFaP9/R0Mzc3lzQryuV0srm5SX2GzWcKhaKo28cCgUDB3isFAoGg2LFYLBxdXcAMteQku2XLrY/kxBJsP7heSNu/+Bf/gl/4hV/gj/7oj3j8+HGezkwguD5qtRqNRoMsy9hsNioqK2/7lATXRK3RMDQ0xMTLl8iyjMViCbrVzs85PTnFbDYxPDyU9INsY2MjU1PTbG9tZfzBPR27uzsMDg7l5Vg3jRwIsLe3x+uvvVZwl4wkSSlrONON5ywsLIAsU1sXPV7ilNToZS96tRJnHkZ0ICic6C9PwqJJIcZ0ko3opBvPCY3m6O22hOM5saM5rubOOHeEr60rzkEBwfDXWLdFJOr+njjRQTfQk9DRcSN0dSV0sWQzmhMiNJoTIt1oDhA1mgNEjeYA8aM5OKitrWV8fJyRqyC8hKM5th2IGaNCY0A+O0AqTTCSqNKCL8PPclejOfnGYrHQ1dXF7MwsOr2OCqsVnU6P2+3i0unEdnTEw4ePMBj0GR9TpVQS8PuLUpgIyDKKO+4sFAgEglz5tV/7NczmoMj/67/+63k99q0KJv/kn/wTvuM7voPGxkbsdjuf//zn+epXv8qXvvQlIJhcvrGxwc5Vk8H8fPBKS01NDTU1wQ96H/jAB6ivr+cTn/gEEBzD+chHPsLv/d7v0dLSwt7eHgAmkwlTHmaZBYLrIEkS73rXu3C53YyOjuJyuYLz44I7TchpMjs7y+7uHkajAaPRSFV3V/r3HUmit6+X8fFxSkpKMOfB4WY2mzk5yaz5odiQFAqUSuWNjRSp1Co8bjeaJBkGiUQTj8fD/Pw8ao2Gvr6+hG6eTEUTCAonmYomQFg4SSaaQITbxB50PqUTTvIlmsSdR11r3KhIiERZJpH4urrjHBYQDH+NHWEpJLIss3nhiMowSTWWkyrPxDU5h24g+n7v9Fy49cfT1IbmKssErkSTqyyTEKqVhagxJt36Iq7mzmCWydl+WDRxmq1Rzh+nvjQY5hshmgDU4cZbWsbk1FSwfcpxEi+aQGLRBFKLJplkmYSOk8cskxBWq5V3vOPtOBwOdnZ2OD09Q6/XYTIaaW9rQ5Um4yQWk8nE5eUlJnMS580tk/L3UiAQCO4xP/ADP5Dwv/PBrQom+/v7fP/3fz+7u7uUlpYyODjIl770Jf78n//zAPzBH/wBH/zgB8Pbv//97weCQS4f+9jHANjY2Iia2fz0pz+Nx+Phu7/7u6OeK3IfgeC20Wm1dHZ0MDs7KwSTe4JKrWYgzfhfMpRKJf19fUxNTfHo0aO0QYXJ8Pv9bGxscHx8EhaVC4os43A4sNls2O0O3G4XcsRjkkKBQpJQKBTRN6USpVKJWqXCaDSh02lRqlQolUqObbYb/dBvNBjY3dujuTm5i8GJEp1aZml1jaOjI/yBAPX19VRXV6ccfcpVNIHM3SapRBMICieZuk2uK5oA13aZ6FYWsg5/heBYTmT4KwTHckJNNVEsLCRttknE5oWDNw728QYCqBUKqIImU/oFcyLRBIgSTWJdJuFzjxBNgOwCYCNyYxIGwEaMV4UCYKuqq9jc3GRxcZHOzk5wnESf0B0MgI3EZDLRlcXPPBklJSXFK5gU8biQQCAQ3CTJGm8lSUKr1aKJbbtLgyQX80DmLXF+fk5paSlnuxsiz0RQML7xjW/Q3t6OwWi87VMRFAm2oyM2NjZ4mENH/P7ePhsb61RWVtLS0lIwh8bpyUkwKNLlQqFQoNPpKC0txWw2YzQaw2KPLMsE/H78fj/+QODVf/v9+P0B/H4/Ho+b3d1d9AYDAb+fQCCAJuTauKHwQrfLxfr6Oufn55SUlNDV/epKvv38nJ2dHRwOBwqFguHeTi6c7qzdino56E5JJ5qE0IVabDLYPF2TTviY9lcZO6mEEwiO6EDyFp1QeGgi0STkMokVTXQ7q3E5G+H8jQjXgiqBYKJamI8ay5Fm5+IcJt6YthwIChOJBBPH9FzyKuAEbpKpk2Mmj200mcxsOOwMWKwMWDJotlpYSCiYSEtLcS4T9epS2GUCQRdNpGCi3VmPcpnoDjbDLpOQYBIK1A0JJqHRnJDLJJw3cyWYRLpM9B4HzvIaVpZXsFgsNFqCP/sol0lIMIl1mVwJJgldJhAczcnQZYLfVxDBJF+cnZ+zvrZOS2vLbZ9KFF6Ph7W1NUZGRm77VASZkum/iXtOeM11dnbv1lyh13b8mY9Tok/9d7cYOHe6sPzIR+7Fz0KhUKS8+NbQ0MDf/Jt/k49+9KMZhWUXXYaJQPBWQZIUQiwRRGGtqOD8/JyF+QW6urO7Grqzs01nZydl5QmCG6+J2+1mbXUVu92O0WikqbGRkpKSlKKGJElB10gqt4wss7+/HxwDuCW0Oh1d3d3IgQBTU1MsLi5yeXGJ1+vBYDBQWVlJe3s7KnUwALZSHwy7zKZ2OBunCWQ+ogOZu02yzTZJ5TZJ5TRJN5qTzmUC6SuGoXBjOYncJMqNTfyXFyzu76OSFCgdl5CJYJJkNAdIO5oDyV0m4a+vRnMycpkkGM2JRX+yR1tbK/Pz82h1zVQZYkKPC+0yuaJQLpN8YDIacTrTvNZbwHZ8TPUdHMEUCASCQvC7v/u7/OzP/ix/82/+TV577TVkWebZs2d87nOf4//9f/9fDg8P+eQnP4lWq+Wf/JN/kvZ4t96SIxC8VdFoNNcOOBbcP1pbWzk/P0va7JCM7u5ulpeX83YeciDAxsYGY8+fMzszQ1lZOY8ePaK3r4/SsrL8OEAkCZVKxbM337z11qid3V1cLhcatZqOjnYeP35MX38/lVVVYbEEguM5QMa1w+H9smjQgaBo4tKY0rboQO5NOimPmaZ6ONKdkIhErTkQXzMM0Y05iSqGgajw00gRIZJ8tOWceTx4AwGaTGa8gQA78wvUqzW87cEAA909vO3BAPVqDeczmTdbxbbmJHK9xLpjMqkZjvraWnftmmEAJInOri5WVlbwen3xNcOhANhYrgJgE3LLNcP5RKlUBhuFioxjm00IJgKBQHDF5z73OX7lV36Fj3/84/y1v/bX+M7v/E4+/vGP88lPfpL/9J/+Ez/7sz/Lb/zGb/Dv//2/z+h4QjARCG6B8/NzfH4f52eZ1y4K3iJIEvX19SwvZSd+GIxGzOYStre20m8cgd/v5+k3nzI5Ocn01BQvJyYYHxvj+fPnyIEA/f39DD98SHVNdUGaIXp6elAqlbcWVnh5ccHY8+c47HYePnxIc0sLRpMp5UhTrqIJBIUTICPRBKLrh9MeOxQKm6f64UxEk0ROhchWlqjnTVAzHBojiSUypyPSVZEMdQIXR6yDI4Spvydhkw1AqUaDWqFgw2FHrVBQolSi6e2hwWSi32KlwWRCcyXYZCSaJBv9uQqAjSXXmuEQ16kZhqDLRKFQ0N3dzbPZRfy+JMJtItEEUosmt1gznE80Gg1ej+e2TyOM8/ISnU6HWp39+5FAIBDcR775zW/y8OHDuPsfPnzIN7/5TQC+5Vu+hY2NeEdsIoRgIhDcAi8nJ2lsaEAG5mZnOT4+vu1TEhQR1dU1nJ2dZu0yaWtrZWdnh0Ca/RyOV4uRjfV16uvraWpspKGxkfb2dvr6+nj8+DHNLS1odYWdu9Xp9UGx5BYEk431debm5ujs6qK7pyfKSZKO+yCaZOI2CYkmqUgmmiRzmaQjmcskErm3J6qCN180Gk28XlXNgMXK61XV1KsTB8Ope4IiTqZOk1iXSYhI0SQfLpOor0uro1wmoQDY8Nf60sQuE0ClUtHV1cUb0/P4d2NajgxJAk81hsT3R5KJaFLkLpPKykpOT09v+zTC7O7uBnOrBAKBQAAEM0o++9nPxt3/2c9+lsarsg2bzUZ5hmPsQjARCG6Bmpoatre38Xo8qFQqToRgIohAoVTQ1NTE3Gzmtn8INvU0NDSwkOjquSyzvrbG6LNnzM3Nsby0xMryMgcHB1TXVFNaVkZpaSlGkykokhRYwJADAU6Oj3k5MVHQ50mGw+Hg8PCQhw8fYs6x8eKmRZPbGtFxltekHc1JJJpA/GiOq641bizH1dwZNZaTjMixnGQkGsvJBkmSaDKZGbBYaTKZUzqfMhZNUrhMEhHpMvE0tUW5TNx1zRm7TMJfJ6l1DhEpmsCr0F+tVkt7RwdnZ2cE9tbid8zFZZIpSlXRukwqKio5LRJ3aMDvx+l0UlZWdtunIhAIBEXDJz/5SX7t136NoaEhfuiHfogf/uEfZnh4mF//9V/nV37lVwB49uwZf+Nv/I2MjicEE4HgFuhob6empobz8/NwqKRAEEl1dTU+ny9rMa22tpbLy0suL4PBhHIgwNLSEs+ejQIwPDzMyKNH+P1+tDodIyMjWder5UpQJDlhdmaW58+fs7OzQ3t7e06tQNdlfm6O3t7ea2exXFc0ySbXBAo/opOKbPNMko3mZEq+xnKS5pgkGcvJhmxEk1xcJunIxGUSSSKXSdTjMSG/BoOBS4U2KJpEZncU2mVSxBgMetyu4sgfOzw8pL6+/rZPQyAQCIqK7/zO72RhYYG//Jf/MsfHxxwdHfEd3/EdzM3N8Vf/6l8F4O/+3b/Lr/7qr2Z0PCGYCAS3RCAQoKKyktKyslsZRxAUOZJEd3cPS0tLkE37uyTR2dnJzMwMY8+fMzr6HJ1Wy8jII5pbWlCp1UgKBV3d3dTX12c1hpItDrs9PHI2OzNzJZJsU1tbw+MnT+h/8CCYF3LDnJ+do9Pp8tZSdR3RBIpnRAeSiybp8kzgei4TyCz8NZLrjOUkaq7JlZBokgu35TJJFwAbcpkAlJSUoNFoOV+ahti3oreoyyQomqQWGG+Cw8MjIZgIBAJBBF6vl2/91m/F7XbziU98gi984Qt88Ytf5BOf+ETO44tCMBEIbom6ujr2dnfx+VIvZARvXXR6HTU1NYlHbFJgLinh8ePHDAwM8PjxCA2NjQUJbE2Gw25nYX6eubk53G43uzs71NbWhkWSQlQfZ8Pm5gZ1dfldZLzVRZPrukyShb9G4uvqvpGxnFy5Sy6TqK9TBMCG8OvNKJVKZmZnXm1wQy6TYhRNKisrObnlHBO73U5JSfDnIhAIBDfNxz72MSRJirrV1KTPPQP4xje+gUqlYnh4OO/npVarmZqaymuZgBBMBIJbQqPR0Nvby3KWtZeCtxYNjY3Y7facrmaqNZr81P9midvtxuVyMTw8zNDwcFGIJJH4fD70+vyH2d6GaJJLrknKY2YomiR8LElrDiSuGU7kMonE19YVNZaTCYnGciD7euFsySYE1jE9hyzLrNvtvLAdsVZdjZzARXbbLpNYdDot9ZoAKysRrp63aM1wRUUFZ7csmOyJsFeBQHDL9Pf3s7u7G75NTk6m3efs7IwPfOADvOc97ynYeX3gAx9IGPqaK0IwEQhuEYvFglqtxuV03vapCIoUSZJoaWkJjubcEUpLS/F6vQUd97kOkiQVzNl106IJZO82yYdoks1oTiKXSaKKYSCj8NdY0o3lJKsXBjLKMfHOzafdBjIczbkKgN1wOPj6/h4TNhtf399j3eMuepeJU6nDoNdzenLKznb2P6c47rDLRKvVEggE8Hm9t/L8Pq8Xn8+H6RZGGgUCgSCESqWipqYmfKusrEy7z9/5O3+H7/u+7+Ptb397wc7L4/Hwr//1v2ZkZIS/83f+Dh/60IeibtkiBBOB4Bbxer1odToODpJciRMIAKvVitPpzC7L5JZwu1xMTk4WdWuD1Wrl8OioYMfPh2hS6DDYdA06IdEkFdmM5kB6l0kuYzlyb+5ZJJnkmJT09WZ93ExcJiceN75AgBazGV8gwEkSG3OxuUwABmpK2dza5Cj0b+gt6jJpbm5mb28v/YYFYP/ggMam64UqCwQCQSLOz8+jbm538pDrxcVF6urqaG1t5f3vf3+0AzEB/+7f/TuWl5f56Ec/mu/TjmJqaopHjx5RUlLCwsIC4+Pj4duLFy+yPl5x/hUSCN4ijI2NUW6xUF2VfnEieAsjSRgMBi4uLm4lJDUb7HYHmqs60mKlrq6OsbExWlsTuxzygRMlevzoNWqcntyuQjslNXrZi16txOn1p93epTGh8zjQK8GZZnOnoRz95UnaY+r9LpzK+PElZ3lNlPsgbj/nWZRrwVndFLc4d9W1pq289bV1oVtZiHNQpELd3wPTc3EuDWlpKWnIar5Q93Snd6R0daGZmkRlNrBmt6NSKCjXxAsI3taOrPJYXFWNUSNMLmtdlGPHVVodJVBdmixsryyxferDajbRXluJwXUed1z9yV54FMup1KH3u+ju7mZ2ZhaNWkNJaUnG5xiFSht0mRgSu1tikZ0OJH3xvP9VV1ezvLxMfUNDXmfl0yLLHNtsdGfQHCUQCIoAgz54K3au3scaG6P/3n70ox/lYx/7WNzmr7/+Ov/+3/97urq62N/f5xd+4Rd4xzvewfT0NFarNW77xcVFfvqnf5qvfe1rqFSFlSC+8pWv5PV4wmEiENwigUAAjVqNw1FcdmNB8WE0Gjm95Zn5TLBYynFeVRoXK5JCgVKp5PLioqDP40QZFE5ydJpA4cNgM3GZZDuak8plkg5Xc+fNj+UUgHQuk0a1hm+prmHIauVbqmtoMpmQOzqixnJCeGOCYiNdJkDOLpOlvUP+w5vT/Lf/8xU+/2dvsLQTdINk4jIxXtjo7u5mcmoS56UzN5dJNhShy0SSJKqrqzm2xTunCsnp6SkVFRUoFKJdTyAQ5J/NzU3Ozs7Ct5/5mZ9JuN13fMd38Nf/+l9nYGCAb//2b+d//a//BcDnPve5uG39fj/f933fx8///M/T1ZW+Ba/YEIKJQHCLlJdb8AcCbG9v3/apCIqcqqoqdnd3i3osRw4EWF1dxedL74a4bSqrqti/oVG4kGhSrA061xFNIHnVcKIsk1zCXxOR17GcLFuo0pFJlokkSVg3thm2VtBsNid1KMS6ZDxNbVFfu+uao77OJsvk5foOEwdnXHq8zGzu8HJtK+MsEwg2EXR1djE+Po7H40m4X4iUozl3OMukqbmZ/f3sf3+vw97eHk3Nzek3FAgEghwoKSmJumm1mY1QGo1GBgYGWFxcjHvMbrczOjrKj/3Yj6FSqVCpVPzTf/pPmZiYQKVS8Sd/8if5fhk8e/aMD3/4w7z//e/nfe97X9QtW4RgIhDcIj093bS3taHRatN+4BS8tdEbDFitVlZWU48w3DY2m423ve312z6NtNTV1mK7wSvDxVw7DKkrh1PlmSRrzcnUZZJJ+GtsW44vw1GEfNYLZxr8GklKl0mKK2yJXCaxXNdlEokc/p9XZOIy0dkP0Ol0tLa2MvZ8DL/WkNRlkheK0GWiUavR6/Vc3JBL1ON2o1Ao0Ovy3/IlEAgE18HtdjM7O0ttbW3cYyUlJUxOTvLixYvw7Ud+5Efo7u7mxYsXvP56fj83fv7zn+ed73wnMzMzfPGLX8Tr9TIzM8Of/MmfUFqa2RhoJEIwEQiKgOqqKk6O0+cJCN7aNDe3cGyz4UkRwHWbSAoFOp0uZUBYsSApFCgkxY0KlcUqmjgNwcrndO05+XCZQOLw10gyCX9NRORYTj7rhXMJfs2oMYdgxXAkiTJWvK0dceGvkeTiMtHtrDLYXEdfQw0ak5khi4nBlqDIlY3LBMBkMlFXX8/42His7hL92u6py6Srq4vV1VUC/kDBn2tvb49m4S4RCARFwE/91E/xp3/6p6yurvLGG2/w3d/93Zyfn/MDP/ADAPzMz/wMH/jABwBQKBQ8ePAg6lZVVYVOp+PBgwcYjca8ntsv/uIv8mu/9mv8z//5P9FoNHzqU59idnaW7/me76Eph8BsIZgIBLeM1+vF7XZzfp75B0bBWxOFUkF7ezszs+lbOG6LioqKOzNiZjAasJ/Hh1wWkrsqmqQazcnGZZKsYvgmxnKuWy+cd/I8xx3rMklHR00l73/nI/4/bxvm//t6Px110U6iTF0mAOVlZZRbyplcXH3LuUz0ej3t7e2srCwX9HncLhfn53YqKioK+jwCgUCQCVtbW3zv934v3d3dvO9970Oj0fD06dOwqLu7u8vGxkaaoxSG5eVl/spf+StAsAb+4uICSZL4h//wH/Lbv/3bWR9PCCYCwS1yeHjI06dPOT07i0ulFggSUVZWhsfjYXcn+2DMm8BqtXJ6Gi/+eYtw5MxqsXByC0G6d100SUYyl0mu5GMsJxMyqRfOBXVPd0YVw7FkEv6aqGI4EldVY9qxHP3uGh01lbze2UJnlQVDxPc3mcskktj2pKrKKtQaNY5cwpTvuMukuroanU7HQYFykeRAgMXFJQYHB262kUcgEAiS8PnPf56dnR08Hg/b29v81//6X+nr6ws//ru/+7t89atfTbr/xz72sZwqfjPBYrFgt9sBqK+vZ2pqCgiGZl/mUEwgBBOB4BYxGk0EZJmz01O83tyqRwVvLQ4ODlGr1ewUqWCi1emQ5QDIMh6Ph4X5BUafPePZs2dFl9Oj0+luTcgpdtEkFdm6TDINf40k17GcSNT9PbeeY5IJsWM5CZ+79Xp1yKnCXwGc5vgKyFicGlPCKumQywSgob6B5Z19LrcSuC3ucWMOQE9PL0eHh/lt35JlvB4vKysrtLQ0YyryWnmBQCAoBt71rnfxx3/8xwB8z/d8Dz/+4z/OD//wD/O93/u9vOc978n6eEIwEQhuEYNBz//z7ndjNpvR6+9AR7vgRvH5fOzu7DA1OYnfF7zqv7e3R29PD1qtjtOT4sy9KS0t5dmzUaanpigpMfPw0SNa29pYX1u77VOL4vDoiNLSslt7/mIVTeDmXSa5juXEkkm98E3lmGTELYa/yrLM5stx3lhcY2nvEFmW0e+/sk879aVRYzmJiHWZALS2tuLxuNnfjxdXUnLHXSYKhcTw8DDLy8vh9+t0BPx+Li8vOTk+Zm93l/X1dRbmF5ienmZmepqZ2VnWN9axWCzU1dWlP6BAIBAI+M3f/E3e//73A8EslZ/6qZ9if3+f973vfXz2s5/N+njFKdMLBG8hZFnGYDBis9morkl8lVbw1kGWZU5PTtje3sHlclJaVobJZGJpeZmSkhK8Xg86vZ7W1hYWFhZ4WJ7eEXDTNDU309DQgC5CBKywVrC9lXrxddM47HaqKitv9RycKNHjR69R4/Rk7zJzSmr0she9WonTm77O2aUxofM40CvBmWRzp6Ec/eUJenw4E3xMcJmr0NsP4hbLzvKahA4ECIa/xo556O22jJwNkegONsOhpr6ubrQL8+FxFLm3J05AyIqFhbznioTGcrIVXOSOjoTCjnd6Lhxo62lqixKI3HXNUbkurqrGqDEml7Uuasxp2q3mTxc2uTCeo1WpYKCLAWP6cQ/9yV6co0hnPwiLaRISRqMJ2XHMiUZLeeR71JXLRCpNLbylRakCf2aixE2i0+no7e1lYWEBSZKoqKigvLwchSJ4fdLj8XB8fMzp6SkBWUalVKI3GNDr9ZjNZqr0BvR6HRqNRozeCAQCQY5YLJbwfysUCj784Q/z4Q9/OOfjCcFEILhFfD4fb775JmXl5VTe8sJNcLtcXlyws7vL6ckJer2e5uYmzCUl4cdnpqdx2O0MDQ0BYDSZUCqVTE9N0dPbi1KZYX/sDaDRaECWuXA42D844Pj4GDkg43I5cTld6PS3X4l5bLPh9/sxFoHF/bZEk5THvBJNUqH3uxI6DPQeR1RIqLOiIc6p4KxuinIzJDzP5k5064vhcRJfW1ecYyIT1KtLaUdaTP09GY3GFArH9FzaLBVva0deR4xsjks8Pj+dtZUs7h5is1+A0Ry3nf5oKxzg69SY4lxETqUubkzLhQqj0cjo7CyDg4OZj5KEXCaGzGofZacDSX/7/4YjsVgsvP7667jdbnZ3d1laWkKWZZAkNGo11dXVtLe3o1KJj+ACgUBQKAKBAEtLSxwcHBAIRLeYvfvd787qWOLdWiDIA7IsE5BllIrsptw2t7aoqqqiqro6/caCe4fz8pLDw0OOjo6QJIna2lo62tshwZXFvv7+uPv6Hzxge2uLpcVFunsKE1yZK8vLy5ydnVFbW0tZaSmnp6dUVlWxurpCb0Qo2G3g9XhYXl5mcGioaK7i3oZoovc4krpMQqRymURmV4TPI4XLJO051bWi21mNy9e4Dur+nqiw1BDS0lLCCt90eOfmM64MzpiurriWnkAgwFP7OQcLizSYjLyzpibsUohFmp2LaglSLcxHheKqVhbwtb1yzujWF3E1d2I1GdColKzNzaO1VmE1G3Garej3N8JtRk59aeJq6AQuk1gUksTjtjqeTUzw6NGjqLHT++wyCaHVamlpaaGlpeW2T0UgEAjeUjx9+pTv+77vY319PShYRyBJEn5/+s9JkYgME4HgGvh8Pt54402ePn3Km2+8kXAbWZbxer1x/2BdLhc7OzvCWXKP8ft8uJzO8Ncet5vDgwMW5ucZHR1lfn4eJImhoSEePnpETW1tQrEkGUqlksbGRs7P7RnPzN8UJpMJs9mMVqvl8OiIgYEBuru6uMhnIGIO+H0+XryYoLOrC61We6vnEstNZ5pA6jyTdK05kDgAFuKzTBKFvwJpw18zIbZeOJMck1y4To5Jtm05Tw8O+H2Xi//f7AL/aXGZb+y9EqFi23IiSdSWE/V1RPhre5WF/+fRQ97eXMN7Brpor86srjZdxXB4O1QoJAVdXZ2MjY9xcXH1O5FJxfAdzjIRCAQCwe3yIz/yIzx+/JipqSmOj485OTkJ346Pj7M+nnCYCATXwOVyodVpaWtrY3ZmBp/PF7bZbmxusrW5iaRQIBGsg+3p6eH09JS5uTmUKhXNTU1IWbpSBMXL+dk5ao0avV6Pw25ndnYWGdBptXi8XpQKBQajkYqKCrq6urISR5IhKRQ0NjWyuLhIT2+BgilzwGKxsLGxQUCW6e7qQqPVIkkSJpOJY5sNizW77Ip8EPD7GR8fp729jbKysht//ky4SadJNnkmCfe/psskk7EcCNYLR47l6FYWonJMYoNOr0WBckyybdjZvrzAEwjQazCwEPCz5QgKjfkcy5EkiY5qKzqdD1dNtHAf5zKJGMtJRKKxnBDlARfd3d28mJhg4MEAJVejhkldJiot+NyZvYgid5kIBAKB4OZZXFzk93//9+nIwUmaCLFSEwhy5PjkhMmpKUpLg7PWZrOZ6elpFhYWWFxcZGtzk/4HD+jv76evr4/TszPGxsc5Pz/HaDTS3d1NSWlmc9qCu4HDYef56Cjr6+usrq6i0+t58vgxXV3dPH78mIePHtHd3Y3Vas2LWBKirLQUlzvDBcYNodZoePLaa3R3d2OxWsOjL3V1dWzdQvirHAgw/uIFTc3NtyLWZMNNOk1cSdwCseTiMkm4bQKXSdT5xLTl5KNeGMhIZEiXIVJoIjNU6g1GNAoFs5eXaBRKGkzGrI6VTVuOq7Qa3c5q+OtMg3jTVQwD4XEurUZLb08vU1NTnJyeCJeJQCAQCArG66+/zlIOjXjJEA4TgSAHDo+OWFlepquzE7VGA0B9fT0Oh4Pg5I1Md3f3q3wESaKnu4e1tVU0Gg1nZ5l/EBTcHcwlJZjNwdDE6upqysrKkBSKGwk5dbtc7O7sUFvk1ZMlJSV4PB7kQOBG3VXz8/NUV1VRVXXN3IQbIuQ0yXn/K6dJJqTLM7mOyyRd+Cvk1paTDclyTHKhpK+X85nZgueYvO3q93RnbZU6tZZ3pmhQ8zS1oYnIMcm2LSdTcgl/jUStVtPb18vc7BydnZ1UlKRozBEuE4FAIBBkwcuXL8P//ff//t/nJ3/yJ9nb22NgYAC1OvoC1ODgYFbHFoKJQJADfp+P8vLysFgCwdGIyFaTWBRKBSUlJezu7gZdKVep+YL7g+IqSKq5uTn9xnlEp9fz6NEjJiYmqKqqQlnM7QuShNVqZWdnh/qG5Bb/fHJ2eorT5aKnyIJx0+FEiV5DTqM5ITINgQVSjuZA8gBYSN6Yk45cxnIgul4YgjkmkfXCmtm5uHyPqOMN9OCanMsp+PUmUCgUvKOmBmpqkJaW4gJfI+uFC0W+wl8B9JcnOA3lqJQqevt6mZ+bx9fSTE158r+ZQFaNOQKBQCB46zI8PIwkSVGZkT/4gz8Y/u/QYyL0VSC4IQxGIweHh9hsNogJc01FRWUl7R0dNLe0CLHknuHzepmdnb21RgS1RkN1dTVb29u38vzZUFNby8FBvCuhUCwtLwfFkjv4by4omtz+aE4oADbhvubkrp1YFwKkH8sB4sZyZFlm6fCYN9a2mTNXRH0gimyEyQsL2VcXZ0K2wa/JSFeRnIx8j+UkCn91KnVJx3JCKBVKenp72NzYxOVKMc6lyiKUWakSYzkCgUDwFmZ1dZWVlRVWV1cT3kKPraykDoZPhBBMBIIcKDGbef3119ne2sJdZNkRgtvB7fbg9XqZm5tjY309/Q4FoKKigmPb9VtHCo1er0eWgyLTjSDLUZWmd5Gbas5J15qTKsskbvsEzoNkwaGRbTmuuta4x5dO7Hx5YY031nf48sIaS2eFaVvKNMck2xDXvI/wJECafTV25K5rjsoxSdWWkw2JRqoy3jdirEshKeju7sbt9nCxdzvvlwKBQCC4PzQ3N4dvv/d7v8eXv/zlqPuam5v58pe/zOc///msjy0EE4EgAS9evOCrX/3TuCrgSBQKBQqFAq2u8PkU9431tbXgwj4Ld06xYzQaaGlt5bXXXmN/fx+/33/jYpreYECt1jAzPX2jz5sL1TXVrK+nH8UQ5C8ENh0hl0kq0STpvuaqrMJfIwmNfaTC5nTj9vnprLTg9vk5dnmyeg51f0/e2mWuUy+ckq6uqODXbEg1fnQdIselnPrEozEZNSIlGOWSJAlJZ8Dr9bK0vJx8ZxH+KhAIBIIs+K3f+q2EY9j9/f185jOfyfp4QjAR5IzT6eTw6AiXy5VSWLhr+Hw+XG43ZrOJpaVlno2O8uabb+J0OqO229vbC7adCLLmyGZjZ2eH58+fc3R4eD+EE0mitrYWlVpNU1MTY8+fM/nyJetrazd6Gg8GHuD1erGfn9/o82ZLdVUVJyfHN/JcsizfnJulQFxXNIH8jeZk6zKJCwetaMh6LMeq12J0O1g8PEarUmLRaaKCTIGooFO5twfNRmrbrW6gBymPKfqFQu7owDUZL6TkK8g2kkKN5SRDQsJoMFKphbm5BK8ny7EcgUAgEAj29vaora2Nu7+yspLd3d2sjycEE0HWnJ2dMTY2xuTUFIcHB0xPT/P06VO++fQpL168YHl5mcPDQ5x3TEhxuVw8ffqUZ8+eUVVZSU1NDSqVkva2NppbWsKVwCG2t7aorKy8xTO+u0hIPBgYYGBggP39fUafP+dgfz/u98VhtzP67NmdE1Sqa2p48tprjIyMYLfbWV1dTb9THmlubmY5hxnNm0SlVmMwGLAdHRX8uaqrq1m/pTGpfBISTXLaN4+jOSn3y9FlAqnHchqGH/EXWmt5vbmO93S10PLwYdTj3s4uluyXPNvZZ/nk7Pp/ewqUY5Iv8pFj4rLWxeWYZEK+xnJCOP1g0BuQZZnJqSkS/uSEy0QgEAgEGdLY2Mg3vvGNuPu/8Y1vUJdDm6SQ4wUZIcsyh4eHrK4Ga3EbGhrQxWYCyDJuj4fLiwuOjo7Y2NjA4/GAJKHT6jCXmCkxmykrL0ejzv0qaaFQqVRIkoK+/ni7tUqtpqa6mvPzc0pKSjg6OkKSJFRF+DruBsGPxBqtlv4HD/B6vCwtLbKxsUF9fT0+n4/Dw0N8Ph9arQ63x4PH40GtUsX/3l3hcbs5OzvD6XLhcjrDYYKDQ0M39qpikRQKtFotciBwY88Z8AeYnZ2lra0wFv180trayuzsLNaKioI+T2NTE6PPnuF2ue78CN11mnMyrRp2aUzoEoS1ho9zVTMcO2aRrGIYguGvsW4EvfMsPOaRri1HkiQ6LSU0JsneWDk+46u7R1zafWiUCmhpINf+G1N/T9rRmILVC+cRd10z2oX5cChubL1wLmTTlqOzH0QFAjtRpXQnNTQ0sLu3x8TEC4YGh5BCIc2iYlggEAgEWfBDP/RD/MRP/ARer5dv+7ZvA+DLX/4yH/7wh/nJn/zJrI8nBBNBSgKBANvb22xublJSUkJnZ2dykUCS0Gq1aLVayi2WV/fLMh6Ph8vLS2zHx6ytreH3+zGZTFgrKrBarGi1msTHvEG2trYwGJIHQ6rVara2tjg9O8PldNJRpHWUd4HYK4hqjZrevj78Ph/LyytotRqGhoc5Ojxkc3OTmZkZVCoVLqeT4eFh1BoNXo+X09MTbDYbjosLFJICo8mI0WCgqqoKk8kU1cl+G+zu7OB0Om9UtFEoFbS0trK1vU1lZSUKZe6uhEKjNxjQaLQc22xYCjze1tzczPLyMn39/QV9nptCr1HnXDecadVwuprhpPvFVAw7y2vici6cFQ05ORVi64VD2JxO3IEAHZZSlo7POHbGO13U/T0wPZezO6PY8TS1oZmdQ+7NX92w02yNqhdOuI3GFD92pdRl7TbSeRzU1tRwcHjI2PgYD4cfxtUpCwQCgUCQjg9/+MMcHx/zoz/6o8GL94BOp+Mf/+N/zM/8zM9kfTwhmNxDAgEZj9eDx+3GneDm8XiQgcqKCurr69FFXHF1uz2cnZ1yenrK6dkZfp8Pi8VCX19f7gsvSUKj1aLRaikrL4f6epBlLp1Ozk5P2d7awufzYTAYqKiowGq1Rp3TTbC/v8/h4SHd3cmvFpaVlaHVanF7PDQ1pQ8pFGSPUqWiq7sr/HV1TQ3VNa+uWh4cHPDixYvgh2hJwmQ0UlNTQ1lZWVxl7OnJCUajMa/nd3lxwfHxMVtb2xiNBiwWC2VlZcHniX3+01O2t7cZGRnJ6zlkQm1tLUqlkrHxcR4OD6NUFe9bfWdnBy9fvqS0rAxlAcWdyqoqNjc3uXA4MJpS53QUO06U6PHnJJqEXCbpRJNCuEwyQW+3hbMzXHWt6HZWw6MirubOqBESX1sXupUFXFWNWPV6tAoFq2uraMorsOh1yHU9aGbn0gaiSktLyEIAD6PbWU3YVJQv9JcncTXVTv+rMbCqykqUCgWjz58z8ujRq/eFyzMwJA6ejUV2OpD0d/vfuUAgEAhyQ5Ik/vk//+d85CMfYXZ2Fr1eT2dnJ1ptFrlYERTvp2hB1mxsbrK5sYFSqUStVqNWa1Br1KjVajRqNUajEbVaHXSIyDLHJydMTk4SCARQKBT4fD7UajVGkwmzyURlVVXhFjCShMFgwGAwUFtXB7KMy+Xi7OyM3d1dvF4vOp2eigorFRUV16oElWU5fPN6vXi8XryeYAWsx+PB7XZzdHREX19f3KI39pz1BgN6gyHncxFckWO+QFVVFSVmc1BQS/WzAg6PjoJCSobns729jdFopLS0FCnBVU2X08nU9DQqpZKOjnZMJhN7e3ssLy8Hx4XUasrLyykvL0elUrG4sMDQ8HDCY90EVVf/ficmXvJo5NGtnEMmaHU6mpubmZmeZmBwsKDP1dnZycLCAsO3+HPJFyHRJKd9MxzNgdxdJnHPeeUyuc5YTiraLKXw9ieczs9jbmygrawko/10Az0JA1WBYI5JV1fix67wzs3ndyznqiknUb2xa3IO3UD+3CPwKsfE1dwZ/Lq0OipsNxFOfSn6o62kFdEp900zlhPCarWiVCp5NjrKyMgIajGWIxAIBIIsMZlMPHny5NrHEYLJPcAfCDA1NRUO0pTSLCQBkCSsVitWq5WA348MBb26m8n56PR6dHp92FHgvhJQZmZm8Hg8aLXasAPFYDDgcDiw2WzYbDY8Xi+xr1qOOryEQpJQqlSoVSpU4ZsanU5HT2/vnV9A3RlkOXGoX4YkyzCJ5fz8nObm5oy2PTs7Y29vD51Ox9LSEiqVipKSEjRaLTqtjnJLOdPT0/R091BS+moh1tL66iqs1+Nhb2+ftbU1XC433d3daDS3O2pmtVrZ29vDdnRU8JyQ61BVXc3h4SF7u7vUJEg1zxfmkhJKSkpYWlqiM81C+C5wnTwTSD+ak85lAqDHl9BlorcfRI3lJCLXsZxESJJEu7UMVUUZ7vLMXAipyCbH5CaQOzriGn28rR0wPRccNcoQV1UjupUFfG35/f13akxxOSZOpQ59TI5JOnQeR7ipqaysDIVSyeizZ4yMjKDJ8k+0cJkIBAKBIB8IweSO4/f7eTY6SnV1dc4Vt8WacaDV6ajS6aiqDtqxPR4PZ6enLCws4HK5MBgMmM0ltLS0oL7lhakgc54+fYohz6MysbjdbpRKZXrBQpZxu91sbGzQ3t4eHBkDfF4vx8fHuNxuFpcWGRocDIoopcmvWqs1GhqbGmlsasznS7k2bW1tzExPF7VgAtDd3c34+DiVlZUFHSFq7+jg5cREwcWZm+Q6ozkZHT+JyyQ0lpMNicJf47aJGMuBYL1wZINLbI6J7mATV1Xm/+7Uq0v3Nsckltjg11zIJMckW9KN5YQoMZtpaW1ldHSURyMj6DIdyxEuE4FAIBDkCXFJ/Q4jyzIvX05SW1OTs1hyl9BoNFRWVdHe0UH/gwe0trVRUVkhxJI7hlqjYbDAoxfra+vU1tQkfdzj8TD67Blf+9rXePrGG7g9nrBYAsFWpKrqapqamlCrVOj1erze3K7i3zZ6vR5/IHCjTT25oFKraW5uZm4u9VV9ORDA5XRe67kGBgbY2tri/CzzqtJi5TpVw5C+ZtiVRtwAko5YxIZ+xraohLeLaF2JXZTHZmmERkdCxDolfF3daHdeVUjLvT1oNl5VbCdzY8S6N7LBOzef875vRWIdSekwGY20tbczOTlZoDMSCAQCgSA5QjC5w6yurqLVaqIbaQSCIkeRycjYNTk/P6MihaNCo9EwMjLC48ePGXjwIK2YICkUmY26FSlqlSpcs1zMVFVV4fF4sNvtuJxOdnd3WVxY4OXLlzwfHeX56CgvXrzgxYsJ7HZ7zs8jKRQMDAwwPz+Px51hLkIRExzNyb7i3Cllvk/slf/wMWJcAiEyHcPIJQcj31wnF6SkL76G/jbxNLUhzaYWHdPhKq1Gt7OacptQjkkmZBsCnGgMzGgwoFap8Hi9wfDXDJGdqUfKBAKBQCBIhxBM7igXl5ccHh7S0HD7HzYFgmyoqKhg7Pnzgh3/wuFAq9MlH+uQZdwuF3a7HY1Gg06nSzm6o1Qqef78OT6fL+ew2tumtbWVhYXF9BveNpJEV1cXCwsLLC0t43a5KCsro72tjeGHDxl5/JiHjx7R0tLM/n7qYMp0aHU6qmtqrn2cYiJX0aSQLpOE26bJRYHgWE4ksUGkOttOxs+XK6b+nmDw61uAyPahXAnlmETdlyLHJtE4V6qA4cbGRsYnZzI/IaWYOhcIBALB9RF/Te4o21tb1NbWpm0KEQiKjcamJg4PDwt2/J2dHaoqK5M+Pj09HW61cbvd+P1+LClG2h4+Kt6GmUxxOp3478g8v9FkSlvFXFZWxu7eXsptMqGutpap6Wka09SE+7xeArKMWqUq2nDo67TmZEq2WSaJwl9DbTlxx07RlhOqFw5/HVMvDLefY5L3tpwbxGWtuxEBKpJM23Ii0Wg06HU6PB4vGlFWJxAIBIIbojg/+QnSYrPZKM20MlUgKDKMJhPf+Po3CnJsh8ORsk7Y5Xbz8NEjHgwMMPL4Ma+9/jodHfc7ALK6uhq/P4DvjuawxKLT6fD7ry8OqNRqAv702S5zc3NMT03x/PlzFovccXCbLpNcue5Yzm3nmBRyLEeWZdbtdl7Yjlhzu5Dz4HJzVTWiWsnu9zgU/HpTJGtnamhsxHFxkflYjlIlxnIEAoFAcC2EYHJHkSTpTmcqCN7adHd3YzBkVg+cLf5AAK0uiQ1clos+/LQQKJRK6uvrmZ6evu1TyQ+ShE6r5fLiIh+HSokcCOByuXj46BGPnzzh9PSMQIRY4/f7i2ZU67oBsJmQLMsEMg9/hfyM5eSTlDkmtyiSbTgcfH1/jwmbjW+cn7N2jeye2ySbHJNUYzlqlYqdgyNc9yB7SCAQCAR3AyGY3EECgYAQSwSCBFw4HOiTiSVc1Q0XsLK2mNHr9bjv0SKjpKSE45PsKm3jkOWUionNZuP58+eUl78K1i4tLeHw8BA5EGBzY5M3nj7l6Ogo/LjP6+Xw4ID5+Xmej46yvLR8vXPMkusEwF7HZZJN+Gu+2nJix0h0B5tJzy9XTEncKInIZ1uOYzoY3HriceMLBGgxm/FarezPRIs33tYOvNPpQ15VC/lv8kkW/HrdHJN01NfXc3lxycXlZcb7CJeJQCAQCHJFCCZ3kIODA/R6McAruDv4vF4O9vfDV+JlWSZQgKvyNpuN0tLSpI9fXl6iUWe/mLwPlFvKMRgMHBUwP+Ym0Wg0127+8Xq9qJSJRQK/z8fy0hJDQ0O0d7SH729pbWVjY4Pnz5/jcjkZHh5mezu4cPd5vbx48YLjkxOqKisZGRnh7OyU87PzjM4nn+6nXESTjI+dg8skHfkeywGixnKAuLEc9WruVcKx5HUsp+vVaynXaFEpFKzZ7agUCspyEHzddc1Z75NJU04inFmMbmVbLxxCpVRiNBpxHx/g8XjS7yDCXwUCgUBwDcRfkTuG4+KClZUV+vr6b/tUBIKMkAMBxsbG0Ol0HB4eotVqOT09xWA05v253G53wmyfgN/P2dkZS0tL9Pb15f157wpdXV2Mv3jB5uYmBqOR7u67GVIJQeG4ta3tWsdYX1+nvDzaGeFyOtne3uH07BSr1Yo6pkFJo9FQV19PVWVl+DGfz4vH48F2dITJZIr6vvb29jI3N5c4PFiWubi45ODwgGObDZ/PR3V1Naenp/T196dsb0pFrgGwQZcJOL3J93VpTEnzJXIJf023wNbbbTjNr0KZdWf7uEqr070UfF3dUa4Kubcno7pdaWkJ+RqZRvkOf20ymfgWajjxuCnXaGnOQ9jxXcDpD45tJXM1eQJgMBgYGxvjyZMnKJMInwKBQCAQXBfhMLlD+Hw+JiYm6OzsRKEUPzrB3UCSJBQKBYNDQ5SVl2M2m3n8+DF9BRAufD4fapUKORDg/OyctdU1xsfHGRsbY2dnh76+PkymwoVXFjsarZaRkRFkWUavL0yGzE1w4XDg9/sxm80JH/dnUAHt9/s5OTmlvr4+6n63x8Px8THDw8O0J1k419fXRwkpFRUV7O3uUllZicMRLSboDQYCgQAXDge2oyPWVleZmpzEdnTEy5cvWVxcQKPR8OjRI3p6elCp1VRXV7OysoLf52N/b4+11dWcclIK6TIpFNmM5UB+64Wvm2NSiPBXSZJoNpsZtlbQbDaLcdwIVCoVtXV1vHjxIqMwXDGWIxAIBIJcEA6TO4LH6+XF+DhNjY3JAy0FgiLk+OQEv9+P3++PW5zmG7VazfJyMDNCq9NhsVgYHBwUVx8jCAQC+Hw+mhozr2AtNtbW1mhtfbV49no8zMzMIMsyPr8fp9PJyMgIBkPy0cXFhQUaGxvCNcEej4eAP0BpaSlqjRqX04kxQ3GtqamJN954g4PDQ1QJRiYqKipYXFxEp9dTWlJCRUUFS8vLNNTXUxFRgV1aVhZ2SO3s7DAxMYHRZOLy4gKLxUJJxLiZLMvIAZmDwwPUKhXWioqo5yy0y0TvcaSsGE40bqH3u+LyLPQeR9hl4qxoSJiJkfQ8YuqFfW1d6FYWouqFtTvrKUdSMqkXNvX3hDNFMqEYKoal2Tnk3szzVwB064u4mjsLcj5OpQ69/SBhpo3+8iRpBk46LOXleNxuZmZm6O9P4bxVquCOVKsLBAKBoLgQgskd4OjoiPn5eVpaWjCXlNz26QgEWbGyssKjR49uRLTo6CzMh/37hEajoa6ujvn5edrb2/H5/Oj0d0iElWXsdjtKpRKX08Xe3h6Hh4d0dLSjUCgwmkxMTEykFEu8Hi8Oh4PunlcLyv29Pba3t3ny5Altra0sLi0xPDyc0SlJCgVPXnst6e94c0sLzS0tUfelO3ZXVxdnZ2c0NDZybLOxs7MTFkzm5+Y4Pz9HBiQkZGQUCkVwvCjGgaDXqHF6br9O2mWuimtKCY3lQFAAWt7Z52JjHWNFDe21lWE3Rb7GciCYY+JpCo5yqft7MgpMzYaSvl7OZ2bzesxs8TS1ReW1ZILLWpfWrROqFo51/lwHJ6qcc28A9LKXmpoa1tbXWVlZoe2aY3oCgUAgEMQi5jqKHJfLxcLCAn39/UIsEWSM8/KShfkFkGV8vtu9qlZiLuH8PLPQS8HN0NDQwOXlJePjL5ienrrt08kOSWJoeJiVlVWWlhaRJHj06CHlFgulZWWoVCpUShUXjuT2+9nZGfyBAKsrK5wcnzA7M8ORzUZdfT2zs3MYjcZgvkiKY8SSb0HQXFJCw5ULyGK14nA4cF+F3JpMJsxmM+1tbQwMPODBgwdsbm7y4sWLqGNcp2Y4XWMO5Df8dXlnny8/f8mf7pzxZ8/GWN4NhhPncywnE8eFbqAHaen6YbCxjTneufm8jOzIsszq+Tljh4esnp9nNIpyG8Q25eSC00/SvByIdkE1NzdzcnLC7u5uymOKsRyBQCAQZIsQTIqc+fl5mltaxEiBIGPkQIDZ2TkuLhyMjo4y9vw5fn/21vx8UW4pj6pdFRQBksTQ0DAPHw4HBbUiXXQlQ6/XMzQ8xIOBgeD7Y8wYTHNzEysria+w+7zBgNYnjx9jNBrZ2dmmvLychw8f0tTUhF6v48XEBD6fj5Pr1hbnkba2Nl6+nGRxcZGdnR0aGxuxVlSg0+sxGAz4fH6am1sS7pttlolTSr99LhXDEBzLibvP48B2Zsft9dHZUIvH58Nmz2xhm2iEpGD1whnkmMCrLJN81gyHWPe4+drOLmOHR3xtZ5c1uz3vz5ENl7oStqZe8MbMIkvbe8iynFVTTr6QgI6ODtbX15PXjYu2HIFAIBDkgPjrUaQ4nU4mXr7EZDQmDTYUCBKxvr6B2Wyio6OD4+NjLi8veTkxQUtLS0LLfr4JXQkPBAKsrq0lrW0V3C6BgJ+XLydpbm4u+O/ETRKq5rXb7UEhKOa1La+s0NjYiEKppLqmhuqamqjHkwW93jYWqxWLxcLG5iYPHz5EFVOPrVBIeL3xFau5ZplA0GWSKssEgi6TRFkmiUg1lmMtNaNVq1jc2sWkUlGviRbxMh3L8bV1oVp5JWz4urrRLsxH5ZjEjuUwPZc2xyRbQqM5IdEkX4Gwpz4fnkCAtpISVs7POXG783LcXFnePeTPXi7i0BrRqlXAIB31NWn3KwQKhYLu7m5mZ2cZHBrCVIAmNoFAIMgagwkMdyFoX3xeT4YQTIqU/YMDqiqrqKisSL+xQHDF2dkZR7YjHo+MgCRhrajACpRbLKyurLCzu0t/X19BF8j7e3vY7XYUCgX1dXVUV1fj892ew0XwCrfLxdraGsfHx2g0GhoaGuIEg7vM3t4emxsbGAwGurq6Ev6eu10uzA0Nt3B2eUCSaGpKnB8xODgYru+ODIcNkW2WiVNSo5dTb59JxXCm4a/tddXAILYzO9ZSMx0aLyEvirO6Cf3+xqvnrWtFt7Matb/OtoPLWpf2dd1UvTAUpjWnTKVCo1Cwcn6ORqGgXKuFHKZMXFWN6FYW8LV1Xet8bHYHHp+PzvZaFrd2sZ3Zb1QwcXr9wZDiK1eUSqWiq6uLiRcvePLkSc7V3AKBQCAQhBAjOUWK/fwco0lcHRFkjv38nIX5eQYHBuIWiiaTiYHBQVRKJUt5mM9PhVqjobKyksGhIeobGlCp1XcrVPQec26343a76erupqmp6V6JJXIgwPLSEnV1dfT390e1z0QSCATiRnjuAwqlkgcPHrCcYBSp0Fkm2ZCoJcVZXoPBe0FHfQ2v93XSUV+TVX1uvsdyktULZzOWky9kWWbdbueF7Yh1u50mtYZ31dXyqLKCd9XV0nLLDlSr2YRGFXQGadUqrKWpzyfWYQRXwa+XycffUuWYJEKr1dLW3s7zsbH4cVSlSuSYCAQCgSArhGBSpFxeXqIT9cGCDLHb7czNzTE4NIRGq026XXdPD5dOJ5sbG3jc7rxnV1xeXLC7s0PgaixCUFxYLBY8Hg9Wq5XKqviF611GUigYGRnB5XLz7NloUmEwEAjc20worVYbzGhJMqZRqCyTfIa/Ru3rPIv+2m6L+lp3tp9wv1jXhK+rG+3OetR9kS0y6v4e1KuFFZJzZcPh4Ov7e0zYbHx9f48Nr4fWkhIeVVbSWlKSlbBUCNprK/kLg5283tvJe0YGr5xCiYl1FWVCpuNesZiMRurr6xkfHy/aYFyBQCAQ3A2EYFKk+Hw+/LfcbiK4G3g9HmZnZxkYGESbQiwJMTgwwLndzsvJSZaXl5FlGY8nPvsgFXIgEM6KiCQQCGAwGGhMMjYguF2USiUajQbn5eVtn0pB0On1tHe08/jxCKenp3GCoN/vx+f3o1Tczz99kkJBS0sLCwuLcY/dhsskl/DX8L4V0WNTN92WAyRvy7lBl8mJx40vEKDFbMYXCHBaZJ8LJEmis6YiJ2dQoSkvK6OsrIypqTvWBCYQCASCouJ+fmq8B/T39zM3N4fb5cLj8SQUTwL+xItWwVsLGVCrVJmPvUgS/f39PH78OFgtOzbGy4mJrJ5zb3+fp2+8wcuJCXZ3d9nf22NxYYGFhcV7Oe5wn6iurmFzcwsICl+HBwesr63d7knlGUmhoLysjLGxMaanpxkfG2P02TNevnxJZUUF0j0VTCCYY+TzeZO6xwrhMoHsKoaTjeVk9DwxLpPwMWPGcnxtXXFjObEuk1SkHMu5Qco1WlQKBWt2OyqFgjLx/hqH0+tPmrdTXV2NQqFgaXk56n4xliMQCASCTBF/eYuUsrIy+vr6WLtayJzb7fT19qLRavH5fLhdLlZXV1GpVDidTh4+fHivmi4EqfG43TgcDszmEuZmZylNEPKYCQMDA6ytrXF6eordbo9uZErQMBIiEAhQWlpKdVVV8Eo+wRaP9vZ2FPd03OG+YK2wsrq6yvPRUSRJoqSkhNOzM1wuFxcXF9TW1lJblz48s9hpb2/H7XbjdDoxGAzB8Me3wHtkWVlZsCo6wWu9TmNOKjIJf01EovDXqH0rGtAfbeHUB9/fYsNfIXlbTiy+rm5UC9E1vzfRlnNdmkwmvoUaTjxuyjVamvf2bvuU7hyNTU0sLS6yvbNDfV1dsF7YX1xOHYFAIBAUL0IwKWLKysoYHh4G4PT0lIWFBeobGtjd2UWr1dDU1ER9fT1f/vKXmZyaoqW5GXNJye2etOBmkCSmp6fR6nQ0NzVTXZN+wZDsOC2treHAWKVSiSzLBAIB3B4PI48eIUlSXC5KaUkJl5eXrK6t8fjx4zy8IMFNoVKpePCgH51OF66mPb8STBobG5mZmbkXggmShFanQ/sWy4KqqKxke3uHleVlWtvaEo5I5NSYo+bGKoadGlNmx4hpy3E1d6JbX4xqy9EdbOKqaozbN5O2HN1AD67JucRtOQsL0HW9hplMkCSJZrOZZoJitrSfOLelELhKq9HtrCYcf7pLSATrwudmZ9HpdFgtlts+JYFAIBDcIe6vL/meUVZWRlNTE/bzc9RqFf39D2hoaECSJN729rej0WiShsTu7uzw4sULfN7MPyALihuNJiiYVVVW5i6WRGAuKWHk8WMePHjAw4cPGXn8mAprBZOTk7x8+TJue5PZTFuSxZig+DGZzWGxBKCktJSq6moMRiNGoxHb0dEtnp3gugwND+H3+5mZmYl77DpZJqlwpRE5iiH8FdKP5WQS/nrTYzlvdZz+7JtyYlFIEt3d3czPzeFwiHEcgUAgEGSOEEzuEDU1NfT09DA0NIRC8WqhajIaMRmNLCwsMDc7y9bmJqenp/h9PuRAAJfbjdvtvtdz+29FtDodfn9+M2xUanXYyt/V3cXjJ09QqdWcHB+zvLTE9tbWq41F88C9pLm5mY2NjfQbCoqazq6uYLhvHv+d3vXwV19Xd9TXcm9PXFtOIooh/FVwfZRKJd3d3UxMTOAP+EWOiUAgEAgyQqyg7wm9vb28/e1v5+GjR1RVV+N2uVleXmZ+YQFkGYPBkHUTiqB4sR0dsbGxQUWFteDPVVdby/LyMjqdjt3d3XDQ8OnpKcJfcv8wGI1IknRvm3TuMy6nk+mrRhCvx4vf78efIBjcibIoKoavE/6a9Bxiwl+BuPDXbCiW8Nf7jhNV0qybjPZPEfwaiUajob2jgzdHx5BlEZovEAgEgvQIweSeoVapqLBa6ezs4PHjx7z25AlDQ0M8eviQpcVF/P78B/4Jbh6dTodKqaS0rKzgz1VVXc3jJ0+ob2jAYDAwNzfP6ckJKysrNDbGZwMI7j5NTU2srKyk31BQFNjPz5mbm2NqaorLy0tevnzJixfjdHZ1ocxzCPNNuUyi9q1oiBrLcVY35TyW4+vqjhvLiXSZQOKxnGJ2mXhbO/BOp85jEbzCaDDQ0NjI2dk5gYBwSgoEAoEgNUIweYug1+vp7u5mbXU1/caCosdoMmEwGNjb3b3R5+3q6sJoNLC0tARIVFbFXyEW3H0sFgtOp1MIrEXM2ekpEy9eMPrsGZubm1itVh4+esTw8DDtbW08fPQIS4pwy0K5TCB1xXAsyVwm+iwyK2LHclzNnXFjOclcJnJvtFMk0ViOcJncP8pKS/n/s/fnsY3t5334/z6H6+EuUhu17+tImn3u2E6Tpl838K9w3BZBmga4cd2mSBOgaZIiRZyldpA4dvJH4ABpL/KHiyRFWgdO4jYokou4X3ztbL73zr5IGu37vpHiTh6e8/uDkkYLJVEUycPl/QIGtiTy8NHMXA355vN5HoPRgFevXkLl8VIiIroAA5MKUl1djWg0ytkTZaKzsxPLx2eKFIDeYEBLayu8Xi8GBwcK+thUQIIAr9fLgLWILS0to6GhAXfv3sXA4CBqamqg0+lgMBphtdmg12uzBO+i4a8RS1XRDn/NpMvkXEXQZUJXZzKa0N5Qi6nzuoeIiIjAwKTi2B0O7O3tIRQMMjgpcaKGQ3wbm5pgsVo1e3zKv7q6Ouz5fFqXQacoySTejI8jGo2kjuRdc1NVNl0mmRzLuWqXidbDX487r8sk3bGcUukyiTW0Xvk+Zv9Gya8UvkgknoBkNiMUDBb8zQciIiodDEwqTEtzM3w+H7a3tzE6OopIJKJ1SZSl9fV1VFWd33JPdB16gwFGgwFhDn8tKtFoFMFgEHfu3IHRaLzWtYp5xfB5w18v6zI5quHU8Fe5o+fMsZxcrBg+UgJdJvrJCa1LKEICOjo7sbq6ii2uUyciojQYmFQYh8OBkZERDA0Nwev1IhgIaF0SZSk1zJFdQpQ/Xq8XiwsXv6ikwrJYrRBE8dytZ2qarTiXuWqXCVC8K4ZPH8vJ5YrhS7tMchWaTE6WTOdKsclkU85pgiCgp6cHU5OT2N/fz0NVRERUyhiYVDCH04nd3V0ezSlRDocDweCpwYj8s6Qc8ng8Z/+OkeYavF7Mz88ffRyNRPDmzRs8efwYHz16hPm5OYRDoYzCk2y6TCppxXCmXSYMOLQXSWQ/pFrU6dDb24tXr16lZr0REREdYGBSwdxVVXA4ndjYSD8oj4qbxWJBPB5HMpnE5sYGHj9+jA8/+ggBvkNGOSLqdJAkiX+nioy3oQGxWAwL8/NIyjKmpqfhcjpx584dPLh/HwAwNTWNJ0+e4OmTJ5ifm4Nvby/nHYXlvmI40y4TRVHw9+vr+F+72/jw2RMoWXT5kHYkpIIWg9GIrq4uPH36FLKc/XBiIiIqLwxMKlxPdzc2Nze1LoOyIQhobmnB8+fPsbu7i6EbNzAyPIw3byaw7/dffn+iS6iqitq6Og5ELELDw8MIBAJ49uwZzCYT6r3e1ABYQUBbeztGbo7g7r17uH3nDsKRCCYmJvDy1SusrqycuRZXDKecHv566LDLRFVVzO3v43koiIXR10fraD/Y3MSfzM3iAxH4871dPHrxLON6KTORJGC+wt+DjK8bP3mER7JY0NLSgqdPnzL4IiIiAIA2ewdJc6urq9ja2oKcTPJJQQmrr69Hff3JtvWhoSG8evUKPT3dqS0aRFfk29vD/MIC4rEYVFVFb2/v5XeiwhIE3BgayuimjQ0NmAqH4XG5UFd/vWMuVxE12s59kRuxVEEK7yGS5mmIlIwiojNfeG0p4kdEcr79OLCDiN1z9LHZv4Gos+7M/eSOHuhn384akXt6YZqcOLFFxrg4i3hLB4BUl0li9A0AYD4QwN+sriHusEJY3wKCQbTa7VgJhxBXFPS7XBgHsBaLpeaZ9JxdZ3ypLOagGOam03bDHGdaXTgzt4XSczidiMcTePnyJUZGRiBccxMVERGVNnaYVKilpSU0NDSgva0Nw8PDWpdDOWSWzBgeHsLMzCxevXqFWCymdUlUQlRFwcTkJHp6enD/wQPcuXMHrqrzj1BQ8YrH43j18iXm5+cxODCArq6ug2HR6RXLiuEzj3Oqy+SqK4Zz1WWyF4shrijocDiQUFX4ZmcAAI0WK4yiiHGfD0ZRREd3V+pOWQ6B5TwU7VXXVMNkNmOyBLYfERFRfjEwqVAqAJPZDL3BAEHkX4NyYzKbcfv2LTR4vRh9/RoffPcDRCMcZEeXC4VCkCQJFosFQGq9MJWOeDyOly9e4PGjRxgfG0NDQwNGRkYgHfx5nqeYVwyfe99LVgxnO8vkvI05VSYTjKKI2f19WBrr4dKnumPeqa3FD7V34Hu9XvxQewfeqa3NbnMOX5wXlcbGRkQiESwuLmpdChERaYhHcioVt6mUP0GAp7oanupq7GxvY2ZmGoM3bmhdFRUzVcXm5iacDofWldBVqCrm5+exv7+PaCyGBq8XjU1NWR0lkIyGM3MdLr2PQXfphhJJl5pDcdzhsZzTovZaSIHNM8dypHgQkYMAJlLdBGn77WydSF0LpI23L2yjDe0wr869/bi1G+aFKUQ9DUefM28uIVrbfPk3eKB7ewNo8GIvFkOVyYQ2ux2x1xMQu7rwsTRHnWyDfQiOvrnweI6qqlgKBeGPx+GMx9B/kx2fxaSjowNv3ryB2WxGbe3Z7iciIip/bC2oQKqqgnFJZfF4PIhEo5ATV3shRJVlcnISgUAAzc2Zv4gkbamKgrX1dSwuLqG2rg737t5FU3NzVmFJPlcMXySTLpPzVgwXsstEEAS0Oxy4XVODdofj6Pf49Nac487rNFFVFYvBAP7flWW8v7SIlxPj+DAYwCLXeGvicFPOGYKAnp4eTE/PwM9h6kREFYmBSQVKJpMXnmOnMiQIaG5qwvTMjNaVUBGrr6+HLMsQeUyvJCRlGU+ePkUwGERffx+qPZ6cHLG86iwToHArhvM9y+SyNcOHG3OO7j+UCkQyDk0OgpOlUBAfbm7gxew0ZtdWYBF1EOtqsBc/f+bURY9B2buso0rU6dDX14vXr18jEokUqCoiIioWfFZcgXZ3d2GzWrUugwqstrYWwUBA6zKoiDmcTpjNZuztnT0mQcXn+fPnqKutRXd3N2pra3MybyafXSYXDX893WVy3vDXtPfNYZfJcZetGT66/tDlQ1ptg31HvzA5Cd/UFDZXV2AWRMQsEpYsZuhFEVVG04XXyeSxCkmK+M8EV1ld55IuI0l3eadSPukNBnR3d+PZs2dIsFOTiKiiMDCpQCsrK6ipqdG6DCowQRSh0+kQj8e1LoWKTCIex8b6Ol69eoVIJIIqbsUpCb19fdjc3MTM9DTUIphLVSldJue5rANEVVUsBAKYrq/GRo0HG2Yjlkx66CCg2W7DJ+rq0WLTLhTQ2kV/D4qBWZLQ2tqKp0+fQlEUrcshIqICYWBSYZKKgmg0CpPZfPmNqey0tLTgzfg4Hj96hJWVFa3LoSKwtraGp8+eYX9/H50dHbh77x6QxfwLKqykLGNtdRWqqiKZTOZ0kHcEuqxWDF+mmLpMDkOTbLpMsj2asxgM4m831vFiZweT/j04jSbcqa5Bn8uFG1VutNrtWc2eKRXpwq9SY3c4UFtXhxcvXhRFSElERPnHwKTCbG1tweVyaV0GZUhVFCwvL2NxYQHLy8sIXXMgoKe6GsMjI2hra7v2tag8WC0WWCQJ3T09sPCoXsl48+YNTCYTbt++jZ7e3qJZD18qXSbHyR09F3aZnB4Ae+iqoclePAZZUdBmt8Ok00PS6yAKAmokCW7T5W9iqKqKuf19PN3awtz+fsFfsEuBnTO/t2lvFz//35Z0IVip8Xg8MJnNmJ+f17oUIiIqgOJ4hkUFs7qygurqaq3LoAyoioLXr18jFAxCp9dDSSYx/uZNTp4kC4LAd8cqQDweh3pB67iSTGJ9fZ1DXkvI+toaHj96hIQso7m5GWKeBnjnq8sEQNF1mRx9fBCanO4yOZTuaM5VQpMqowl6UcR8IIAas4RP1Hkx4vFkdBRHmJ7Gelsj/mZ1DU+3tvE3q2uYv8ZMKvPm0pnumlw678+oXDQ1NmJzc5Obc4iIKgCfJVcQVVURi8V4HKdEKIqCWCyG3r4+NDY2oqW1FZLZjN2dncvvfAlRFBmYlLFgIIDR16/x7OlTLC8vn/m6qijY293D06dPYTKbMXjjhgZVUjaWl5dx8+ZN3Lx5s2i6Sk67qMvkosGdWneZnPg4zZrh086bZ3JeaNJiS80pGfF48In6ejysq8NNT3XGR3H2YjHEFQUdDgfiioK92PkbdSjPBAFdXV14PToKWb58LTYREZWu4ny2RXkRDAYhWSxal0EZikSjZ55Ex+NxWHNwbEIQBITDkdTsAyorr1+9wvT0NBobG3H//n2sr29ASSYR2N/H4sICnj9/jidPnmB5eQk3btxAa2ur1iXTVQhCTrbhZKqUu0widS0nukyiDe0Zd5kcyuZoDpA+NBEEAa12+5VCkuPXqDKZYBRFzO7vwyiKqDKZYJibvnAQLeWPwWhEc1MTXr9+rXUpRESURwxMKsj2zg6cDofWZVAGwuEwxsfG0T8wcPS5RDy1ytAsSeff8ZyukcD+/omvWaxWRCIRxPgOZdmJxmK4eesWXFVVEEQRHo8bT548wfz8PERRxODAIO7eu4eh4eGL/y5RURIFoWCbrrJZMZyJQnWZHN3mnKM5mXSZpJPJ0Rwgs0GwmTIP9aHNbsf3NHhxu6Ya39PgRZvdfml9lF+uqiqIosgh6kREZUyvdQFUOLFoDHZH+idYVDwWFxexubGB3t4eWI51BImiAFmWIScSUFQVuoNjNROTk4jHYlBUFfFYDJ1dXXC73dDrdEfbTl6+fAWz2QSLxXJ01KeurvbE9alMnArNOjo70dHZqVExlEuqokCn0yOwvw9PAWdRSUYDIgeBbSYiggGSAYgkLu5gk3RA5JybSJAROfYUJWqvhTmwefJxquoh7a2fvW/Ej4jkTN2mrgXSxuLb6zS0w7w69/bj1m6YF6YQ9TQAOBgAOzuJaG1z6uOeXpgmJxBrSHViqf19EMbfwLg4i3hLB4BUaJIYfQP97BSmquuwF4uhymRCm90O81Afoq/eHIUmalfXhb8np53uUGl3ONB+we0PpTtCdBHzzuqZAIky09raitHRMbiqqmDlv6lERGWHgUkFUaGifBcWlo+trS0MDw/DaDKd+LxOr0dzczOeP38OnU6HZDKJlpYWCABu3boFCAL2/X7Mzs5iaWkpNexTECAKAkwmI0ZGRhCNxaATRej0ehiNRm2+QSK6kngshlevXwOqCqfLBdslA0JzKQIdJOT+6F7UaIP5nG0qEUsVpPDe2fvYayEFNhHRvZ3DdRiaRA66ViLVTZC2z87tkQI7iNg9Rx+b/RuIOuvefryzehSaAKmjOYehCZA6mnM6NDnOMNiHicfP8fevXyHicsMoikAD0O5wHHWaHAYnmYYmh2HJ4f2B1Cyy+UDgKJTp5iyqnIjEE5CM2XVVCaKIrq4uvHzxAg8ePOAQbSKiMsPApIKoqprxmWnSTktLC6amptIO4qz3elHv9QJIbcuYmppKzaA4+HN1OJ24eevWude2FXD2ARHlxs7uLpwOB7q6tesAKIYukwuvEw+eCU2u2mVySO7ogX528u3HPb3QT06cCE0AnOgyAYBAcyPiuz70REKYlKzYi8VOdINcpdskXVgCAPOBAP5mdQ1xRYHk24Xe5UT6g0OlQ4J84VGsUmCWzKirq8ObN28wcOwoLRERlT7G4BXEbDJxZkUJqKmuRjKZxNbmJmZnZvDq1SuEgmffia33etHc0oLa2rMDEYmoPCwvLWFtdRVNTenncxRCMc0yidprz8wyyfeaYeDsPJPDIy/H54W4TSZIDXWYiUQh+XZRdapLEEgFIIe/hOnpc38d3ua0k5tyVASaG9N+78Um3fyZjO+bn79+OVddU4NINIrNzS2tSyEiohxiYFJBqqqqEEzzwpuKjCCgv78f8/Pz2NnZQVNjI8bGxtLetKWl5czRHapgbM8vO+FwGI2NTecO6A2HQgX7c89mY85FK4aPrnuFjTlHn8/RmuHTA2APQ5PDAbCXbc0B3oYm7U47vq+pEQ9GBvEPXE50b58MZE47Hp6c/nWek5tyBLjz9PP/dJiUC+m2HWV83wvCtWLS2dGBqalJvjlFRFRGGJhUEIfDAZ/fDyWpaF0KXcJgNKKnpweNjY2ocruh05XIW2ykKVVVj45nUXlobWvD2trJzgdVUbC1uYn5uXk8ffYMgQIE4cXWZXLmtlmuGT5x3Qy35pwXmgiCgA6XA3fratB79yYEQUi7Pec6Djfl3E9E8Q9cTrQ7Tw5yT7chx7S6cO7Gn4uc/v05TYr4z91MVKlEnQ4dHR148eJF6ucxERGVPAYmFUSn06GrsxOLi4uX35g053S50NCYarcWRBGBQEDjiqjYqSoHO5cbk8kEWU51WSjJJMbGxvDq1Sssr6xAUZLo6+3F3OwsYtHsjzxcRT66TKJGW0G6TI5uc4WjOXJHT9qjOelCk9MuWjmcrcNNOXcc9qNQ5rSrbsjJJSkePDe8qhRWmw1OpxMzMzNal0JERDnAwKTCuN1uxONsFS01vT09mJqcxOTEBI9d0LnYYVJ+QsEgZFnG6Ogonjx5AofdDq/Xi5sjI+jo7ER1TQ1MJhNej46mNmPl0XW6TDI5mpP2Ma/ZZRKpbjrTZXLiOpcczTl00TwTIBVSpOvuyEdokuuulXKR7d+xfPB6vdje3kakQEEmERHlDwOTCqOqKvhyu/RYrFbcun0bkiTh0aPHaYfAEiWTSYgMTMqKyWRCU1MT2lpbce/+fTQ1N6OmthbCsdWlvX198Lg9mJiYKMgxgGy6TC5zUZdJxFKVcZdJpKr+RJfJ0e1ycDTndGhyvMsEyCw00c9OYW5/H0+3tjC3v3+lPy9VVbE09hpP9gNYamzgkY80Mvm7VhCCgIaGBiwuLFx+WyIiKmpcK1xhTCYTkrIMJZmEyLkYJUUQBDQ3N2NndxeJRObrPaly+P1+WCwWrcugHNIbDGhqbr70dm3tbZianMSTJ09wc2QE+jytEI9ABwkXrwo+j2TQ5XTNcNReC3NgM/1t06wZPnObwA4ids/Rx2b/BqLOutS1D1YNRz0NAM6uGj50etWw2t8H4/ibE+uGgbehycTj5/j7168QcblhFEWgAWh3ONJ/w6csj4/ir31+JGuqYVxeAdCIDtfb+6YLa9Ixby6dmc+ilXJYKXwel8uFpeVldCsqRJFBNlHFslgBayk8N2MfxXn4O1OBGhoasLW1rXUZlIWNjQ0Y9Hq4qsrzCSZdz+7uLurq6rQugzSiqoDVYoVen//3QvLVZXLu/XO8ZviyozmHrjrPBHjbaZIuwAg0NyJZU42eSAjJ7S3sZbhNxTA3jb1EAsmaanS6nIgnFeymue/p+SWna8uWFNg583tWCJKudDbknCEIcFdVYWMj9xuHiIiocBiYVKCGhgZs7zAwKTWqomB6ehpJReGRHEorFArB5XJpXQZpQFUU7O/70d/fl/c5NvmeZZLPAbDHQxMAGR3NyWSeSbrQBDjb9eE2mWDUiZh32CE11KFmawOGuelz55Ic/1ptfy+MOhEzPj+MOvHESuGLukuy2ZBTaSSjIS+boOrq6rC0xEH7RESljEdyKpBer4fNasXOzg48Hs/ld6CiIIgi7t2/j3AohLGxMdy4cQMSj1/QaZxhUrH0BkNB//wlowGReObHAyOCAZJ68e2jRhvMaWaQAAezTMJ7Z++T5mhOpKoe0t76hUdzInUtkDYWMzqac+jwaI55cwnR2tRRKbmnF/rJibTHc4TxNzAuzh4d0UmtAW7EbiwGt8mEdqf97aab0TdnvrfDozwA0K6qZ+57XLbbcU4HQrkmJaNph/RWAr3BAFHUIRQOw8p/r4mIShI7TCrU4OAgtre2uKq2xBiNRriqqtDV1YWZmczOq1MlYVhS0Qo4BLQUukxydTQn2tp95mgOkF2niSAI6HA5cLeuBh0ux4m1wIbBvjO/jjvvvpnOLrnI6UG3lDv19fVYmJ/XugwiIsoSA5MKJYoivF4vopGI1qVQFqqqqhCNRvK+RpRKC5tLKpeg0R++VrNMTocmhx0M2RzNOW9rzmXzTICrhya5CDfSSdddkqv5JaVAMuiKZ0POKQ6HHT6fD0n+e01EVJIYmFQwWZah46ac0iQIqK6uwdLy2c0PRFSBNAhM8tllcnzNsKqqmJ5fxIfPX2J6fhFhyZX+PmmOfaTrMjkMTc7UdMV5JlcNTc6ba3IdxsXZC4/inJ5fUkwbci5zUZdRSREEeDwerK2uaV0JERFlgYFJBZNlmauFS1h9fR12tjm8l45hiwlpIB9dJkfX1gEzC0v4v3/3AT589hL/9+8+wMxCKqC4ytEcKc1clHRHc46HJsDZozlA9qEJkNtuk3x1q5j9G2cCo9NOD88FDlY5n3MMKu01MlgpXLIbck6pra3F8vLS5TckIqKiw8CkgiWTSXaYlKi11VVsbm5CFPnnR2+pisJjWlRQ2XaZRARDRl0mALDj8yEej6O7vRXxeBw7Pt+Fa4bPk8nRnBPXOmeeCZA+NDnudGiiqipm9vx4tLqB6XovlL7U17MJTo7f57zuEtPqQt6345zXqUNn6fR6GI1Gzo0jIipBDEwqmCzL0ImF+Svg8/kQCgYxMz2NjfV1yInMNyvQKaqKxcVFyLKMlpZmrauhIqEkk1AUBUKB/pum4rK5sQFRoz/7CHRX7jI5lMkA2AaPC0ajEVNzCzAajfAcrM6OWKrSdplE7bXnDoBNF5qcuN2peSYRbxumtn14/vIFpjd2oKpq2gGpckfPiS4T4GRoMuvbx/83v4xHq5v4/+aXMevbP3NM57Lw5HRQctWtOKfrA/K/IYfe8nq9mOfwVyKiksO1whVMLmCHydrqKgwGAxoaGpBIJDAzM4NkMomOjg6YJakgNZQsVYWiKEfHp2RZhsFgQEdnp8aFUTHh8brKtri4iJGREU1ryNea4e7WZvw/H091mnhcLnS2ngyKJciIpHk6IyWjiOjMbx/vYNXwiRoOVg1HJOfJ+x6sGp7Z2Mb/uxGGsrMJ48oWcPsWuuo8R+uGo56Go/vIHT0wz06m6j62chgAAn//IZJ7PnS1tWN614/dSBSdVanHPB58HK4hPk8mIclF3SXpumEu25Bz+phSsckkdCsGVpsN8/PzkGUZej2ffhMRlQr+xK5gyQIOfVVVFbdv3z76uLm5Gdvb29jY2EBzS8sF96xsiXgc4+PjiMfjuH37DkRRQDgc5pMtSotHtCqXIAjn/jxXVRXBQAB2hyNvjx+BDhKSWd1XMugQSZx/35jJjqHOFkSSZ/+tiFiqIIX3znw+aq+FObB5JjQBDmZtnJqNIUX8R6FJpK4F0sYipMAOdgIhxGQZ3X29mH8zgZ1gGF11ntRjnBOa6GcnYd5cOgpNAMDV2wvT7iPMzc/BWFUNt3SypkNX7Ro5LV+bcU4fV8pUuq1F+VCsG3JOq66uwcrKKlpb+byHiKhUsHe7ghVyS46qqlBV9cTnqtxu7PM877lCwRCePnsGr9eLmpoaPHnyGI8fP8bMzAzq6uq0Lo+KEGe+Vi5BEKCc+hkLAEuLi3jy+DFGR0cRDoXyXke+B8CmvcYFR3PO3PaCoznp5pk0igmY9HpMrW1B9NSiAeEz1zx9rCXdINgOtxPf9/AeHlS78AMWEf1pQp7rOgxL0nWXpDuOUygXzZU5TyE35EhGw7U2Pl1FTU0NVldXCvJYRESUGwxMKlgh5x1UVVXhgw8+QCwWP/qcThShE0UoyezelSx3ZrMJktkMn8+H1rY23Lt/H3fv3cOt27dRV5/5JgIiKk+HPzt9e3tIyDJ2trcRCr4NApKyjKXlZdy6fRutra1YW8vvWtNCDIC9yFW25pyp4Zx5Jl01VfhHQz140NWKfzTUgy6P89IhsMDZ0EQQBHR6XLjzsQdou3MLgiDAtLqQs46Qi8KS0zUdSje/JJMNOYVSLhtyjhN1IiSLBT6fT+tSiIgoQwxMKtjZ9yLzp6GxESaTCYpyMhzxeDzw+c+uJ6TUVP2h4WEIgoDnz59z+wkRnfDo0SM8ffIUCwuLuDkygqWlJYyNjWFnexvhUAjPnj1De1sbdDodvF4v9vb2gDRdKLmWzwGwF3WZpHPY3ZDJquFIddOZdbnR+lYMWUU86G5DV30NYo0dAC7fnAOk7zQ5+lpP74mhsNcJTi4LSy7qLrlsfkm+ZLJSuFx56+s5/JWIqIQwMKlQhdyQcygWj8NsPnlu2+v1YnVlFRNv3sC3l/sW5VInCAK6urtRX1+PJ0+eIBYtzHlwKk0FeC1MRUJOJGAymXD7zm0MDw8hFovBYDDg9u3bWFhYwOjoGPr7++FtOJivIQgwmUzw5zmgvk6XyWUOOw5ycTTn0OnQBMCZ0AQ4Ofg003XDwMnQJNfByfHbX7ZCON2w13IgGXQlM7/kkGSxIBqNIsFtgUREJYGTIyvU6XkihSAgFQAcZ7FY8LGPPUQsFsOjR4/gqqrMd5wuU19fD4sk4eXLlxgZGYHRZNK6JCLSkF6vhyzLCIdCmJ6ehizL6OjogE6vx+07d9Lex2QyFeRFWmrNMK60MefQZQNgo0YbzGlCjhPXuOLWnONDYA+35pw3BDZiPxj42tAO8+pc6giLMzVT6ig0STMIFkDaYbBHtzkWeJgmJy78/s67XzrndZdcZ52wFPGfOcIkxYNpjzpRerW1tVhaWkJHR4fWpRAR0SXYYVKhDAYDkskkQsEg4rFYbo57qCpURYGcSCAWjSIcCiGwvw/f3h62trZgMJz/LlAikYDE9cIXcjid6O3txYsXL5GU05/VJ6IKIQjo6+/H2NgYEokEbt++fWHgHNjfh8/vL+jP2WwHwBb6aM6Za1wwBPayThPgYHvOzuqVjuicuN1B10kmvy5y+DjndZecPo5z+vsADtYrZ7khpxRke3zsujweD9bX1zV584qIiK6GHSYVrLOzE9vb24jH44jH41BVFYlEAsMjI1e+lqqqeP7sGSSLBXqdDjq9HnqdHnr9wf/X69FzwZO7xcVFxONxLMzPQ28wQK/TQxQPulEEAan/Jxx+iKqqKogF2vBTTBxOJzo62vHs+XPcuX27YEN7qVTwyXclsdlsuHvv3qW3W1lZwcb6OoZu3IBksRSgsuzXDEcEAyT14s6Uwy4TSQdE0jzE4arh010m560aPuw0Ob5q+DqdJsDblcPmndW03Sbm2cnU7dJ0m+TCRWHJRd0lxTDwVdIVduBroTbkHCeIIux2O3Z3d+HxeAr++ERElDkGJhWstrYWtbUnz3Z/8MEHWV0rHovBarXi7t27Wd2/t68PsWgMiUT8RICj4uD40MG7MKoKRCJhzM/Po6a2FgJSrenmCupO8VRXI5FI4Pnz57h16xZ3yVKKqp5ZKxsJh7G8vIyWlhaYTs0PosypioLV1VX4fD50dnbBLJXW7+X6+jpGhoehv6DLL18ko6GojuYchiZpb3/saA7wNjQ5LhehCZAKMg6P6AC5DU4u6yw5rC3fTnfz5Pz6GXQiFbOamhqsra0xMCEiKnIMTOikUy++1YNjNpd1c5jMZoiiiK2tLdTU1Fz5YXWiCItFAnB58KGqKmZnZxEMBKCqKtbX13H79u2KCg7qvV7EEwm8ev0aQ0NDWpdDRSAWiyEei+HZ06dIJpOAIMCg18NqteLly5e4d/++1iUWtcNQZHdvD3IigWQyCUEQjn5VVVWhrq4Or1+/Qn19PZqa89MZkHMHIZoWYUk+u0wOXdZlcp15JsDb0OSwywTIbWgCIKfByWVhyXndJemO4+TCRQN3c6HUBr4eZ7FYEAgEtC6DiIguwcCEThAgICnLiMVi2NzcRCAYhF6ngyAIqK6uhtvthizLWFhchF6vh9FgQL3XC51Oh/aODoyOjsLlcl04r+TaNQoCOjs7jz4ORyJIJGQYNDqLrJWWlhZMT01hcmICPb0Xn2Wn8mc0GjEyMgKDwQCDwQBBFBEKBjE2NobBwUGtyytakXAYExMTkJNJeDwetLW2wmgywXjwe3halduNx48fF31gsruzg/n5eSiKgrq6usvvkCfZDoCNCAZIBmTUZXJZaHLmfpcczclHaAKkhsECyCg4Aa4Wnhy/32Ubcc7rLjl9HOf4vJajz6XZIHRVxbJSWKv5JUcEAaKogyzL0Ov5dJyIqFjxJzSd0NzchBcvXsDtdqO5uRkulwuCICAWi2NtbRVvJiYQiUQwMDAAi2RBIBjA2NgYOtrbYbVa0dbaitevX6eOihSIZDYjEgnDYHRefuMy09XVhadPnyIWjfLIRYUTRBFW29sXeeFQCKNjYwWdW1FSVBXT09Pw+/3o7u6Gw5nZz4+kLMNQAi9urFYr5GQS1dXVRTFQW6ujOdnOMzkdmgA4Mc8EOD80AQDz6lzq4wy7TYCTQcfp8AQ4GaCkXVF8SVCSzWacdANf023IKVVazC85zuGwY3d3D7W1V+/MJSKiwij+Z31UUF6vF/X13rcDVw+YTEa0tbWhra0N8UQCxoMOErvdBqfDgbm5OczNzUNVlYKfx21ra8PTZ89wY3Cw8oagCgJaW1sxNTWFGzyaQ8csLCygp7ubYckpciKBtbU1bGxsoKamBrevODxZUZQz69GLkclsxq2bt7C6uoKlpSVUZ3FUMlfyfTQnarSlAo4LHuIq80wOQ5MTn0szBBZIH5oA2XebHEo7rPVgUOxFtznPYViSrrvE7N+49rBXrhTOjsPpxM7ONgMTIqIixsCETkid17/4NsZTx22sVitu3LgBAEgqCnQFDi0kSYJep0M0FiuKd1ILzePxYG5uTusyqIioioJQKHThmtlyEwgEsL62hlA4fDS34+2GrZSELEOn08HjduPGjRtZDYs2SxKSipJ6jEyDE1XF3NwcgsEgbty4UbBg12A0oLWtDdvb25ATCU3mmByXTZdJJkdzjq6fxTyTqL0WUmDzRJcJkPnmHODqoQlwstsEOD84Oe4qAclxF4Ul50l3HKcQMt2QIxl0JT2/5JDNasXS0sVrpomISFsMTCinCh2WHGc2mTR7bE0dnIMmOrS1vQ1LBXSWbG5sYGV1FUlZhmSxoNpTjabmZoiCcJCZHGzXQmpYtMlkgi4H68idDgd2dnbgqa6+9LZrq6tYXl5BTU0Nqqur8fTpU9y8dSsndWTKZrNhd3cXtVrPMkEy70dzrjrP5Oj6p47mHH3+nHkmuQpNAFw5OLmKy8KSi7pL0h3HKReazy85IIgioKqavNlERESZ0fSn83vvvYfh4WE4HA44HA48fPgQf/mXf3n09T/7sz/DD/zAD6C6uhqCIOD58+cZXfdP//RPMTAwAJPJhIGBAXzzm9/M03dAxcLhcCAYCmldhobUy29CFSEWi2Fhfh69ZTwIeHdnB48fP8bu3h76entx9+5dDA4Ooq6+DpIkwWQ2wyyZYZYkmCUJkiTBYrHkLKSoqanB+kb6rSKRcBgvX7zA40eP8NFHH2E/EMDNmyNoa2+Dt6EBLa2tGf9bliv7+wHYbJe/a59v2c6LOOwkuGyN7GWdCRFLFSTIZ+93sMnl9Brcw2Mmp2d0HM7wOD0A9TBgON2dEW1oTwUn/o2022iird1vw5Od1axmjaSTSVhyFVLEf2Z+ybm3TUbzviHnurSeX3LIZrfD7/NpXQYREZ1D08CkqakJX/nKV/D48WM8fvwY3//934/PfOYzGB0dBQCEQiF8/OMfx1e+8pWMr/nd734X/+Jf/Au8++67ePHiBd5991388A//MD788MN8fRtUBJwuF0IVHZgQpbooxsfG0NnZCV0JDCbNxsL8PBYWFnBj8Ab6+vpSM1oKPFPE6XQiEg6f+FwymcTLFy/w5s0btLS04O7du7h39y56e3thMBqPblflckEURWysr5++bN4IAoqq4yibd/czPX4RNdogXfI6WIvQBDg2EPacoCJdcHLV8OT4/Y5f7zzpukuuchznqgNf0/3eVzqnw4HtHW2OQBER0eU0fUb96U9/+sTHX/rSl/Dee+/hgw8+wODgIN59910AwPz8fMbX/OpXv4pPfvKT+PznPw8A+PznP4/vfOc7+OpXv4r/+T//Z85qp+KSasFnlwVVtsWFBVgsFrgLPHi5UOZmZxEOhzEychOiTru8XxBFGI3Go+1U8VgML168QFt7O2qODVdNNxx2PxBALBpFQs7/C8dkMomx0VEYjwU26ezt7iKZTEIURQiCcPC/IianJiEKAhRVhQAcrKw2QhBwdORJVVWoSB3H7OvruzS8yvfRnKPbZTnPJJt1w5kezwFOHtEBcOaYDnCyI+T4cZ0Ttzk4upP2axnMKrls0OtVjuNcdeBrMawULiZ2uwNra2tal0FEROcomrcgk8kkvvGNbyAUCuHhw4dZX+e73/0ufvZnf/bE537gB34AX/3qV8+9TywWQywWO/p4f38/68cn7RT/3gqi/Nn3+7G9vY07d+5oXUpe7Pv92Nvbw61bt4piG1ZNTQ1WVlbg9Xrx6vVr9Pb2wpnBamK9Tg+dTge/349qTzXMUv7WgSficYTD4dTfCUGAqijY2dlBKBQ6CjpCwSAURYHFaoWqKFBVQFUVqKqKpsZG1Hu9AFL/RkcjUcRiBx0YggABqSHhgiBgc3MTCwsLaG1ru7Su627NydU8k3yHJgAgbSymPj411wTAubNNTtRz3nGawy07VxjkenTfC47inNddcrqTRkvXHfhaLPNLDok6EclkEqqqlsQGLiKiSqN5YPLq1Ss8fPgQ0WgUNpsN3/zmNzEwMJD19dbX11F3arBdXV0d1i9of/7yl7+MX/3VX836MUl7qoqCt+UXE/bWVDZZljExMYmhoaGy/O9gbXUVKysrBd0wcxm3241nz55je2cHzU1NGYUlAGCxWtDY2AiD0YhXr16io6Mjo+Gx2TCbzXC73Xj69ClEUYROp4PNbofVYjl6YWa1WFBTW3vpCzWdTgerzQqrzZr261arFc+fP88oMDmU9dacDFcNmy84LnLRENhchSbA5d0mQCo4AdJ3m5z7/WURlABvw5JsuksynV+SrUw35ORCscwvOWS1WrG/v5/xzxEiIioczQOT3t5ePH/+HD6fD3/6p3+Kz372s/jOd75zrdDk9BO/y1L7z3/+8/i5n/u5o4/39/fR3Nyc9eNTYe3u7mJhYR61tdptgCDSSigYwtT0FJqaGvParaAFVVEwNjYGVQVu3ryp+Vrc40xmM4ZHhiEnZFismc8H0ev1aGhsBJCahTI6Ogq/34+Ozs6c1ebb28Py8jKi0SicTif6+/ths9vz+u613mCA0+nE+traUVfKRa5zNCfTVcNRoy0Vbpxzs8PQ5HSXCfA2NDlznwtCEwCQtpevFJoA1wtOruKysOSqq4TPm19yeg4MXc5xMMeEgQkRUfHRPDAxGo3o6uoCANy9exePHj3C7/zO7+D3fu/3srpefX39mW6Szc3NM10nx5lMJpgqdSVtGfD5fHA4HBBEAWurq/A25HYtY9FSVezt7SEhy1CSV29vp9K3sryM9fV1tLa2ovrY7IxyEAwGMT42jqamRni93qLsnLnuIFWj0YibIyOYnpnB40eP4HQ6EQwGIctJOJ1OdHS0Xx4SqSrGx8cRjycgywmoqgqr1Yqm5mY4nc6Ctvi3tLTg5cuXqKury6gTKNujOYeuO88EuDw0kQKbZ9YNnxeaAG+7TQBkfETn6PFOBSdAbsKTTLpKDsOSdN0lF23HOW9+SbFvyCk2DqcTMzMz6Ozo0LoUIiI6RfPA5DRVVU/ME7mqhw8f4lvf+taJOSZ/9Vd/hY997GO5KI+KkNfrxcTkJCRVxebmVsUEJtFoFJOTk3C73WhszG+rNBWnzc1N3Lp1C2KO1uVqTlWxsrKCzc1NCIKAwcEBWKzpj4CUC0EU0d3dDTmRwNbWFhoaGiBJElZWVvDq1StIFsvRBrDGhoYT3RuqoiAQCCAQCGBoaAhGkylnq5OzYTKb0dzcjFevXmF4ZCT9jVQVSUVBUpYhJ5MIyDKqJCPWt7aRTCaRPAh/BVGEKAgQRBF6vR5msxlmkwniQRCTq3kmh9LNMwGuF5pc9YjO0WOemnFy9Pkrhicn7ntBWHK8tnwqhg05ktFQdMdxgFTnmZxIcI4JEVER0jQw+cVf/EV86lOfQnNzMwKBAL7+9a/j29/+Nt5//30AqaMWi4uLWF1NTYGfmJgAkOoiqa9PvavxYz/2Y2hsbMSXv/xlAMB/+A//Af/gH/wD/OZv/iY+85nP4H//7/+N//t//y/+9m//VoPvkApBkiTcPHhyvrFx/jC7ciPLMixWK7p7erQuhTRUDmGJ3+fD4uIiYrEYPB4P+vv7YZYkrcsqKL3BcCLsbWpqQiKRgMPhQGdHJxQliampaUSjUbS1p178jo6OQhAEtLS2ptYrF4F6rxfBUAhPHj8+9sJPeNsgJAgQBRGiKEDU6aATRVhaGuB2OjC/tHwUiKiqevQrmUwinkhATiSgKKmBtIqqwmg0YqijBcFoHA6HAwAQCAZhkaQTwdF1hsAeOj3PBLheaAJc3G0CnAw5TocnmcgkJAFS3SXnhSXndZdcdZ3woetuyLnuwNdiJkkSQqEQbLbCzHEhIqLMaBqYbGxs4N1338Xa2hqcTieGh4fx/vvv45Of/CQA4M///M/xuc997uj2P/IjPwIA+MIXvoAvfvGLAIDFxcWjJ1gA8LGPfQxf//rX8cu//Mv4lV/5FXR2duKP//iP8eDBg8J9Y6QZSZKwu7sLt9utdSl5J8uypu8mE2UrHothe3sHu7u7iEYjsFgsaG5pgcvpLMqjN1oQRPHUXBMDBgb68fTp06OhsfF4HMPDw0U12wXA0THbq5CQRMcVjiMoioJwOIz1QAA1VhMeP3mCuro6rK2uQm8wQEkmIUkSqmtqUFNTg4jRBlPUD5MAxNSzPzez2ZwDZB+aACe7TYDzgxMg8/Djqq46t+S4dMdxOL8kew6HAzs7OwxMiIiKjKCqKhdsnHI4qdy/tnj0rhWVhmQyiY8++ghdXV0wmctrAOZp21tb2N3dRU9vr9alkEaePX2KW7dva11GWofHRXw+H0KhEGKxGGRZhiiK0BsMcNjtqKqqOpg/VBybb0rB7s4OlpaW0dvbi5cvX+D+/ftlEzJJSF55ACwASGoCyWQST1+9RltbO2xWK5KKAr/fj92dHewH9gEIMBgM6K134+WbKahQYbNa0dLSemIWzeHmnHSdJodDYE+HJgAg7aVmp50OTY6+nmauyYmvH3SbABcHJ7l00dwS4OLZJVI8eG5gct78EgnyuR0mmW7IuU6HyeE64WI8kgMA8XgciwsLuF2kP9Mpxywc8Asce83l95fda67D7233L/47HFcYDq+V/VAY7v/fu2X5Z3FdRTfDhOg6dDod2trasbu7W/azTGRZLrp3lqmwFEXRuoQT9v1+LC0vIxIOQ6fTQZIk2Gw21NfXw2KxpIZrl8mLe624PR7s7Oxgamoytc2tzH4/s96aowPu3Rw5mmeiE0W4q6rgrjr7Av0T9x0IxmXs7O5ienoa8XgcTqcT7R3t1+o0Ac4PTjLpNgGQccfJdVwWlACpsOTcr2V5HKcYFGtYAqSGQF9nhh8REeUH39ajsuOqciEYLN0ndJlKJBIwMjCpWKqioBjaA/f9foyOjuLxo0dYWl6Gt74ed+7cwa3bt9HX34+m5mZUud2pjq8ye3Gvle6eHgwND5ddKHz4YvawE+BK9z3oOpAMmb0gthn1qK2pxfDwMG7evAmrzYrnz55hdHQUPn3qncB0Q0oPuyfOO3pyFJykCRWOVg9H/OcGEpG6lrfhSWDnWkdmTjt+vUwGvJ7XXQKcvx3n3McugoGvpcBoNCISiWhdBhHRpd577z0MDw/D4XDA4XDg4cOH+Mu//MsL7xOLxfBLv/RLaG1thclkQmdnJ/7bf/tvBao4e+wwobIjmc2IJ67e1l1qEokEzzpXsEg0CoNem8DseCeJxWKBt74eVVVVPFpD13adVcOHm3Muc3oIrF6vR4O3AV6vF9vb23j9+jXMJjPutHvP7TQBkHZ7DnD5XBMAF3abACcDjePHdYCrd54cD10yCUouO4pz7v0uOI4D5Gbga7k7nGPS1MTNd0RU3JqamvCVr3zlaG7ZH/zBH+Azn/kMnj17hsHBwbT3+eEf/mFsbGzga1/7Grq6urC5uQlZLv5AnYEJlSWdTgclmSyLDSLnkWUZRqNR6zJII9FoFAZD4X6E7/v3sbS8xJCE8i4CHSQjsppnEhEMkAy4cNUwkH5zjgABNdU1qKmuxtr6Ov5mdBoPultTtVywchg4O9fkotAEOBmcAOfPNgHOhifZdJ1kujL4oqM4R9e6YndJLl13fkmxczpdWFtbZWBCREXv05/+9ImPv/SlL+G9997DBx98kDYwef/99/Gd73wHs7OzR8s52traClHqtTEwobLkdDoRDAbhcJbvUK1kMglRLN9AiC7msNsxMz2DcDiMfb8fLpcr56t4w+EwFhcWEAwGITEkoQLLZp7J0X0NuqxCkxQB3novPG4PXkxMoK/WCZvNhliaF+vXmWsCnJxtAlwcnACZBx/ZOKohy+6SrB83w4Gv11XM80sOmc0mhMNhrcsgogq2v79/4mOTyZSaQXeBZDKJb3zjGwiFQnj48GHa2/z5n/857t69i9/6rd/Cf//v/x1WqxU/+IM/iF/7tV+DlOPnr7nGwITKUpXLhYmJCbS1t8NZpqGJw+nEzu4OHE5Osq5EeoMBXm89pqemYLPbsb29jXg8DkEU0dXVBbvdntV1E/E4FhYW4PP7YTQY4PV60dPTU9bdWlR8Do/mZD0EVk1cMzRJzZMYGhrC1tYmkvt+2Kw2yPqzR3AuCk2Aq3WbZBqc5NplYcmhi7pLstmOQ6cIAvR6PeLxODtIicqFxQpYrVpXcTk1NWeuubn5xKe/8IUv4Itf/GLau7x69QoPHz5ENBqFzWbDN7/5TQwMDKS97ezsLP72b/8WZrMZ3/zmN7G9vY2f+qmfwu7ubtHPMWFgQmWpuroaJpMJo6NjcN5wlOWwSSWpQK/jf8KVrKm5GU2n/mELBAKYnZmFChXdXV2wZjDnJplMYnlpCTs7OxBEEfV1dWhra+MWJtJULuaZXDc0AYCamlpEohGE9jZgNslQzWf/m7pKaAKc320CFDY4yTQoOW+NMOWew+HA7u4u6uv5+01Ehbe0tHRirfBF3SW9vb14/vw5fD4f/vRP/xSf/exn8Z3vfCdtaKIoCgRBwB/90R8dvZn927/92/ihH/oh/Jf/8l+KusuEr7aoLImiCEEQEI1FEYvFUhs6yowsJ2CxFP9edyosu92OkZsj2Pf7MTU1BUEQ0dbWCpvdDt2xLpFIOIzllRXs7+8jHo/D7XZj8MaNS9suiQrp2vNMchSaSGYJydpmxLdXIMg+6GxOCDgZxGcSmgC4sNsESB+cALkNT05cN4Ow5MKvXzLsNReuM/C1VOaXHHI6ndje3mZgQkSaONx6kwmj0Xg09PXu3bt49OgRfud3fge/93u/d+a2Xq8XjY2NJzr/+/v7oaoqlpeX0d3dnZtvIA8YmFDZWlldRU93d1mGJQCHvtLFHE4nbt66Bd/eHtbW1hCbn0cyqUBVFaiqCpPJhJqaGrS1tiIej2N6epphCRWtbOeZZLo5B7g8NNHpdNDVNUPdWUPCvwvR6oRef3aDzmFoApwdBgtk1m0CnAwychWeXCUoAd6GJdl2l1y2Tli6Qg6S7cBXoDTmlxyyWCwIBC8OqYiIipGqqojFYmm/9vGPfxzf+MY3EAwGj7Z8Tk5OQhTFoh90zcCEypacSJx4R71cbG5swGAwIJFIMDChS7mqquCqunh+gMFoRCKRAFS1LI+vUWm7zjwTIPPNOcDb0OR8AgRPAyT/JkIhHyI6E+y2k/OCDrstctFtcnT7C8KTE7c7FqSce5sMghIgs7Akk2Gvl80vKcTA15IiCBAFAbIsnwnkiIiKxS/+4i/iU5/6FJqbmxEIBPD1r38d3/72t/H+++8DAD7/+c9jZWUFf/iHfwgA+NEf/VH82q/9Gj73uc/hV3/1V7G9vY2f//mfx7/+1/+6qI/jAAxMqIzEYjEEg0FEo1Gsrq3BoNeX5ZGV1bU16HU6BAIBdgRQzhgMRoTDYVhKYTAZVZxChyZSPJi2y+RQ0lkLh24bgUAQq2uraPA2nL3OsW6TsGjCzPIadvz78Dgd6GzyQhCEjDbpnPleLgg8DtcUX3a7y1ylsyTfx3Eqkd3hwN7eHmpqarQuhYgorY2NDbz77rtYW1uD0+nE8PAw3n//fXzyk58EAKytrWFxcfHo9jabDd/61rfw7//9v8fdu3fh8Xjwwz/8w/j1X/91rb6FjDEwobKgqioePX4Md1UVDAYjWltacr5itVjoRBG9vb0wsLuEcshms8G/v8/AhIrWdYbAHspkngmQWWgSt1XDIYgQxSAmJibQ29t79joHocnK4jz++qOXCCYBkyH11Kur+W3IkukxnctcJyQ5lGlYctnskmLYjlNq80sOOR0ObO/sMDAhoqL1ta997cKv//7v//6Zz/X19eFb3/pWnirKH1HrAohyYXd3F06HA03NzairrytIWBKNRDE1OYnHjx5heWkJPp8PgVO7y/NBFEUk5IvPhRNdlcNhRzgU0roMogulhsBm9yL4cAZGpgNEo0YbJN3FczZiVjfsdju6W7x4+uwZksmzCUvUXovVhB5xOYEbTTWIJWTs+M/+WxGpqn/bcRIPXjpsNR+uEpZc+7EyPDErGXQVM7/kkM1mg9/n07oMIiICAxMqEysrKwV/JyaRiGN/fx+3bt/G7t4eVldWMDY+nvYJcy7pDQZEwuG8PgZVnng8DgPXCFOJKGRoAlz84j5qqYLoacS9wV4kgnsIp/n57HE5IdjcmFxahU0HeJznbyA4DE4iVfVHwUm+w5Pjj5HpgNfLuksyugbnl6QliCJUAIqial0KEVHF45EcKgvV1dXY8/kKepzAbj8Y9KeqGB4eBgBMTU5ia3MT9V5v3h63qbERExOT8FRX5+0xqPLEYrGynPlD5Scn80wyXDcMXL4951DSVQebbgvhYAjAyf+WOpsbAQA7Pj88LicGXQYI5wyEPVHrqeGwR5/PUdBw4poZBiWZrhHW+jhOqTObzIhEI7Dy5zIRkabYYUJlob7ei92dHahqAd+NEQR4vV7Mzc8ffaqtrQ2rq6vY2d7G5MQEgoFATh5KSSqIRiLY202tiI1GI3nvZKHKkkwmIZbhVikqT4fHLIqp0wQAZHsNBFGELhE58XlBENDV0oQHw4PoamlCzFGXul4ymtmmmWNdJwBOdJ5ctfvk9P2OX/fS+2ZQaybdJVdZJ5ytUp1fcsgsmXlMkoioCLDDhMqCKAqw2mwIBoNvOz8KoK6uDs+ePTv62GA0wuFwYGtrC+FwBHIOZo08+ugjCKIIvV4Po8GA6poadHV1cf0r5ZTH48H29jZqa7nxgkpDsXaayM4ahPbW4XToEbtg9kYm64fT1n0q3DjdfXLV+2fqMCzJVXdJJsdxMg20zq2jBOeXHDKbzQiGQhz8SkSkMQYmVDb2/X50tLcX9DGXl5dhNp8cMNvV3Q0AePH8BYxZbLKZm51FKBSGKAoQRRGKouDB/fs5qZfoPG63G3Nzc1qXQXQlxRiaSGYJPqkKr8an8D0DnRAgIHLB063DAEIKbKZqyjA4OfoesgxAriLTsCTT2SVXcZ2Br6VMMpuxvbOjdRlERBWPR3KobFTX1MDv9xfs8dZWV+Hz+XDjxmDar3s8By9AMzwmpCoKAoEAdnZ20NXViba2NjQ2NmLoYD4KUV4JAgRBQDwe17oSoiu5bhdBPo7nuFwutDS34MPppdRtMwgSovZaRO21GR/TKYTDWg5ry8Rl3SWFOI5TDsxmiUdyiIiKAAMTKhuNDQ1YWVkp2OMFg0E0NDScezSmqbkZoihieXk5o+v5/X6Mvn6NxqYmmCUJFqsVdoeDgzipYKw2W0FDR6Jcuc66YSA/oYkgCjDo9UcBggQ5q+BEq/DkKkdwgNT3l+mgV27HuZyoS3WYEhGRthiYUFmYmZ3F6OgYPB5P3h5DVRREIpHUYFlVhdlsxvb2xe2yfX192NzcvPTawUAAc3NzcDid8OZxww7RRcwmEztMqKQVU2hiMpkQDIWwu7eLiKXqRHCS0fWPdXUUMjg5/lhXCUsyut0Vukskgy7r4ziS0VDS80sOqUBhh9kTEdEZnGFCZcFmsyHujqOuPn9nuVdWVrC2tgbhoKPEbDajo6PjwvsIoghBFDE+Noa6+npUuVwQxJM5ZTwWw8rKCmrr6tDY2Ji3+okuYzQaEYsxMKHSdN15JkB2M02At+t5j881cTqcGBkexuTUFObn5yHLMtrb2tBiNRwFDBfNNjl6jFPDYY9qveKsk8scv3amQQnwNixhd0nuGQ0GxOJxmE0mrUshIqpYDEyoLLirqjA1OYlQMIie3t68PMbW1hZu3boFveFq73jdunULvr09rKyuYnZm5ihwAVLvHulEEZIkoT6PYQ9RJiKRCCQeAaMSpkVoApw/DNZkMmPoxhAAIJ6I48Xz53CO3ITRaIQU3ksdY8nwqdjpEONwSOxR3VkEKKe7Vq4SlABXC0s4u+TqzObUamEGJkRE2mFgQmXBYDDgE5/4BD569AhKUoGoy+1ps3gsBlVVrxyWHHJVVcFVldm7b0RaCYXDXGFJJa/YQpNDRoMR3d09ePXqFdxuNyKRMJxOF7qqLAd1X+0p2WUBSjbXuIqrdpYAmXeXXHedcLk4XC3sdru1LoWIqGIxMKGyIpnNiMdjMEsSVEVBNBaDJEmX3/ESfr8fNps9BxUSFa94PM4OEyoLxRqauFwuVFdXIxqNwuttwM7uDv56dBXv9LRAMhzWnt1Ts+uEH1d11bAkm+6S68wvKRdmScL+/r7WZRARVTQGJlRWHA4nxsfHoagqVFVFPB7H3bt3YTQaAQC+vT1MTEygra0NdXV15264Oc3n88Hj4Ts8VN4EIOM12ETFTsvQBEg/1wQAWltbj/5/VVUVoo1RvJieRiKRwIPuZkjiYf3F+RQtm84SoLCzS8ph4CuQehNofW1N6zKIiCoat+RQWWltbYHFasWtmzdx9+5dOOx2xKKpM9pyIoGpqSkMDw9jZ2cHT58+QygYyui6wWCQLbFU9vQGA9Y3NhiaUNk4fOFcyO05hzJZPQykjl3cuHEDHR3t+LvxOSxHU6tkM11DXEjZhCVX7S7hcZy39AYDEonswj4iIsoNBiZUdgQIR2v4TCYTpqam8ezpUzx//hxNzc3Y2NiEnExClhNIyBc/EYnHYlhfW0MymTyz3Yao3AzduAG/z4exsTGtSyHKmWIJTS4LDpxOF0ZGRjA7M3tmDbHWwcnxGrIJS67aXZLtcZxyxPiaiEhbxdnvSXQNgoCjwKSjsxORSAQ2q/Xo+M3Y6Cg8Hg+qPR6YzOdsFVBVPH36DIAKh9OJGzduFKh6Im3JchJeLzc2UXnJ1fEcAG9njVzxiM5Fs00OGY1GCOLbo6LHwwkpvPf28wV6+nY8qLnqEZxDVwlLrttdUk7zSw7pdDokZBkGPZ+yExFpgW+ZU1kJhUIIBAIQxdSTLp1OB5vNdmJWidlshs/nw+bm1oXXSSTisFgs6OrqgjkHg2OJiplvbw+PHz+G212F2trCDY8kKpRcdJoA+e82ESBgZ3fn7OOm6TrJV+fJ6Y6SbMKSbNcIX7e7pFzmlxySDlYLExGRNhiYUFmxWCwwGAwXznJtaGiARZKwvr4OJfn2rT45kcDy0hKePH6MqelptLS0IHIw/4So3MmyDJfLheaWloyHIROVmmIITS6bbTI0NIS5uTlsbKynf+yDACNdeJJtgJLuGtkGJUD2R3HoLLPZjBADEyIizbC/j8qKIAhobm7G6toaGhsb097GLElo7+hANBrF2toaNjY2IQhAUlHgrqrCzVu3oNPpoCoKlpaXEY1E2GFCZc/j8WBpaUnrMojyLhfHc4DsNugcOr5+GDh5TEdv0MPldGJ1dRV1dRcfj0sXaBw/unMV2YYjZx4/y7BEMuiu1V1SjsdxAAYmRERaY2BCZae5uRlPnjyBz+eDy+U693YtLS14PToKVVHw4MGDM0NdBVFEX28fxsbGcPvOnTxXTaQtQRSRTCahKgoHHFPZK5bQBMCZ4GRhfgGyLOPmrVvZ1ZSj4CMbWneWlNtxHACQJAk7O2ePaBERUWHwWTGVHUEQcPPmTSwvLR2tFE7HarPh/r176OzsPPcFosPpyFeZREVHr9cjHo9rXQZRQeTyeE5EMEAy6LIaWnrmmI4ABAJBvHzxEuFwGE+fPoVaArtSrhOWXLe7pJwZjUZEYzGtyyAiqlgMTKgs6fV6eKqrEbvkSYYgiqi5ZMClwHkOVCHEgy4TokqRq9AEyH6uyaHD4GSgsx0fu3cL4XAYL1++hNFoLPrjctcNS+gCqdV/WldBRFSxeCSHypbf50Ndbd21rrHv98N83uphojIxPzcPn28P0WgUsszAhCpLro7nANc7onPoMHT4/k88hKqqUKHibz54BKgqmltaIKB4QvxcHcG5bneJZDSU5XGc4xRFhSgWz589EVGlYGBCZUtRVYi66zVRra6tobqmJkcVERWXly9eIB6Pw2q1oa+vD/F4HDabVeuyiAouX6EJgGsHJ+Z4EN/zzj1EIhG8fPECIyM3r1VfruQiLGF3SWZ0ej2SSRmiyGNLRESFxsCE6AKhYBBdnZ1al0GUc36fD8FgEEPDw7Db7QDAbVBU0XIdmgC4drcJ8DaQsAoiejtaoVPiiKs66HTahA25HuzK2SWX0+t0kGUZBgN/r4iICo0zTKh8XfPMbzKZhCCI0PMJSslIJpOYmpyE3+fTupSi9vjRI6ysrGBgYAB2mzbbLIiKUS5nmgDXn2tyXNRoQwAmvFndhhwOQEhEj8KLfJN0b38d1nLta+Zo0GslHMfR6XRIyLLWZRARVSQGJkTnWF9bg9vt1roMylAoGMSzp09hMBgwOTmJQCBw4uvxWAzhUEij6oqLIAjo7u6Gq6oqNVCQiI7kIzS5zhad42pra9HT3YOdhID5vSCiRtuJMCOXAUq6kOT4Rp9rXZtHca5Ep9NDTlyv64mIiLLDIzlE59jZ3UV7W5vWZVAGtra2MD83j4GBflhtNtTX1+PVq1fo6OiA3+/Hns8HAaktMDdv3dK6XM1ZbTbs7+/DU12tdSlERSmXx3OOrpmDgbCHFEU5OpJzOsCQ4sHza0jzsBeFLLk6dnPi8Q5nu+Sou6QS6A6O5BARUeExMCE6h5yQYTYX70yHcCiEN2/eYGhoGIYKedKYzu7ODhbm53H7zu2jFxBmScLAwADm5ubgqa5Ge3s7BFHERx9+iH2/Hw6HAyoqd2V0ZX7XRFeTr9AEAKSDH9nZBid6vR6hcDjt1y4KOc4LU/IRjKR9/ByGJYfK/TgOwCM5RERaYmBCdA5FSUKvL84nYkoyifHxcdTU1ODVq5cYuXlTswGAWvLt7WFmZha3b9868/1bbTbcGBo68bmenh7Mz88jHo9DlmX09/fD6XIVsGIiKiX5CE2A62/S8Xg8WF1dvfLjFioYSScfYUml0Ot1PJJDRKQRBiZEFxDE4hzzMzExgdraWjS3tECn1+P1q9cYGRmuqHkU+/59TE1N4ebNW9DpM/tR5qqqSs3tQGpLzMrKSkUGJi6XC4uLi3A6nRxqTHSJtzNNDj7OdbdJFsd0jEYjBFFEQpZhyPDnn5byEZZUynEcILVWOB6Pa10GEVFFKv5/ZYk0kIjHi7ZjY31tDQlZRnNLCwCgsbERm5ubiEajSCQS2NjYwP7+PgRBgCCK8Ljd8FRXwyJJ5RGoqCp2d3cxPTODkeHsjyM5XS5MTU0hmUwW7Z91vtTV10MQBDx/8QI3b96EvgRecBFpLZ/dJsDVj+nU1NRgeXm56Gdt5bOzpBKO4wCptcJhdpgQEWmCz5KpbF1nqfDC4iLq6+tzVkuuhEMhLC0t4e7duyc+X+Vy4cWLF5AkCfX19ejq6gIEAXIigdXVVUxOTECWZTQ2NcHr9V74GKqqwre3B5fLdWmHzcb6BpaXlyCKIkRRhNFoRFdXFwxG47W/1xM1KQqCwSB8Ph82NzdhMpkwMjwMk9l8res6nU48efwY7R0dqKmpyVG1paG2rg5+vx9bW1uX/p0gopR8hSbA1Y/p1NTUYHR0tKgDk3yFJZXUXQKkOkw4w4SISBsMTIjS8Pl8aGtt1bqME5LJJMbGx9E/MHAmyGhrb0dbe/uZ++gNBrS0tqKltRWrK6uIRaOXPk4wGMTExAR0Oj3q6mrRcs7vQ1KWsbAwj3v37kEQRSSTSWysr2NiYhI3hm5k900eXjuZRGB/H3t7e/D5/UjKMkwmE5wuF4aHR3I25La7uxvr6+vY3NgoSGCSlGXs7OwgFAqhvaMj74+XCVEozmNnRMUq36EJgIyCE7PJBABF2yWX75klldJdAqQ6TOQEAxMiIi0wMKGypKrZ95dEwmEYDYbimu2gqpicmEB9XR1stuyG9u359tDY0HDp7fx+P7xeL5qam/H0ydNzA5NEQobRZDoKb3Q6HRoaG7G9vY3FhQU0t7RktIUmFAxie2cHkXAY0WgUiqJAVVWYzWa4PR4Mt7bm78WAIGBhcRH6PL/YiMViGB8fhyzLcNjt2N/fL4rAJBgMouXgaBcRZS6foQmQeXBit9ng8/vhcbtzXkO28h2UVFp3CQDodHrIMo/kEJUiweqAYLVqXcalBLVyQuirYmBCZUlVs18Zu7W1BXcRPfkMBYN4MzEBm9WKpubmrK8TjURgdzguvd3e3h66u7qwubkJvV6HWDSa9uiLnJShS3NkZ2hoCJOTk3j29Ck6OjuhKiqi0QiSySSSigJFUaAkk6n/VRT49/fR2NCAhoYG2Gw2iAV+p9TpcKC6ujqvjxEOhWAyGnHz5k0AwKOPPkIkHIZkseT1cS+yurIKnU537WNNRJUqX8NgTzzGsdBBOpYTHIYn0VgM9ixD9Fw7DEqA/G/CqaTuEgAQRQGKomhdBhFRRWJgQmVJVRWIWQYmwWAQDRl0YhSK378Pt9uN9jRHbjKWSpAy6tSIxWIwSxK8kgSoKl6/fg2z2Yy2tjZYjz0xl2U57fUEUURvXx/CoRBmZmag0+shSRL0Oj0Mej10Ot2xX3p0dXVlvOUmHxqbmrC4sIDqPB7JUVX1RIDnqa7G6NgY7t65U9BBvKqiQBBF7OzsYH19DSMjIwV7bKJyle9uk6PHSdN1IicSMOZ4ZtRVFTIoqcTuEgCAIFxrLhsREWWPgQmVpWgslvWTyFgsBouG7/yfJooCktcc9ubz+WDN8HvSiWJq6GtVFbwNDfA2NMC3t4fJyUkYDAb09/dDp9cjKcsXdoNYrFYMDQ9fq+5CsNvtiEQiR2FCPpwOTDo6OhDY30cwGITNbgcA7O7sIBKJwOPxwCxJOa9hZ3sbExMTaGtrw8rKCtxuj6ZBFVE5KVRoApwMJQZ7uzMeEptrhQxKjqu07pIj1zhqTERE2eO0PypLkXAYpoOBeFeVTCZhKKL5JVVuN0KhEF6+eIF4LIZgMIjEFZ6Qx+NxTE5NoTnD4zwDAwOYnJo6EdK4qqpw6/ZtuN0evHjxEpFwGDqdDnKZrDmUJAl+vz9v1z8dmACAt6EBa2trRx/PzMxgfz+Aubl5+Pb2sLmxgeWlJfj29gBVxb7fj/GxcYTD4eMXzuhJdDgcxuzsLO7fvw+fzwe3241AMIDVldWcfY9Ele7tEZ3C/Puxvb2NiaU1RARDasOOQXfiVz6cvv7hYxeCZDRUblhCRESa4duLVJbi8Tg2NzcRCoWg0+lQU1MLh/Py+R0AYDAY4Pf74aqqynOVmTGZTBi5eRO7Ozt4/fo1DEYjEvE4bt+5k9H9Jycm0dnRceI4zYWPZzbD5XSmBgl6PCe+1tDYAKPRgImJCSQVBc4MZqKUgqamJkxOTkIUxYNgQ4AgAK2trXCf+j3Ihslkwu7uLgKBAOwHHSW1NTVYmJ/H0uIi3G43BEFAd3cXxsbHsbq2BoNeD5PZjInJSeh1OhgMBng8HkxMTKCpsREAsLK6CiWZxNDQ0NlVzqqKaDSGUCiIubk59Pf3Q28wYGBwMPVlRcHTZ89gNBpRXZPfGS5ElaIQc00OLS4tYejG241kJ+adHDu2c6bGDDtR0t2/kJ0kx1XsURwiItIcAxMqSw0NDaivr4csy9je3obP58s4MGlra8Pc3BySMzOoqa1FfV0djFl2q+SS2+M5evE+8eYNVldW0HDwwvk80UgE8XjsTPBxmWg0CsfBC/vTqmtq8jrvQwtOlwv37t8/8bl4PI7nz56hJR6HwWDAxsYGmpqa4HA6r3x9u8OBhsZG+H2+o8AEgoC7d+9ibn4ek5OT6OzshN5gwPCpY0wupxNWq/Xo+IwgCPD5fFBVoLmpCQDw4sULNDQ0IB5PwOGwQ5IkTE5OQlEUSBYLurq6jo7+HBJEETdv3sTTJ08YmBDlWCGO6KiKcm435HnBxkVBSqbXKLTDsKTSu0tUpO9WJCKi/GJgQmUpFApBEERYLBICgQDMV9gE4nS5cPPWLSSTSSwvLeHV69fQiSI6u7revtjNNVWFLMuIRCLQ6XSwXLJ+rKOjE8+fP780MJmcnER7e8eVB4tKkoSdnR3Ue71Xul85MRqNGB4exvLyMhKJBJwuFyYmJmAwGGC12dDd1XWl31fJbMbenu/E5wRRRMcl64VPBzTp/szNZjO2trZgMpmO6u3q6oLT5brw2oeDd+PxuOaDI4nKzfHQBMh9t4maxUyLYglBrqrSwxIg9fM6mUxCz9lTREQFxZ+6VJZmZ2cRCoWgKApMJhO6u7uvfA2dTofWtja0trVheWkJuzs7qcBEVREKhWG1XWGnuqoikZARi0URjcbgdDpgMBoxNzeHvd09KEoSoijCaDQiEolAkixIJOJwu93weDywHRyn2dzchMPhQDgchsVy8WDQcCiEZDKJqirXlb/3lpYWjI6OVnRgAgBmSULXsb87jY2NUJJJPHny9MrXMplMiMfjuSzviNVmOzpy5b3ihidJMiMUDMJYRKu0icrF0RGdHHebqKqaVWBSangU5y29TgdZlhmYEBEVGH/qUtmQZRkTE5PY3/cjFArh5s2bMBgMqfbVa7awyocbYVQVW9vbmJ+bx737946+rqoqwqEQ/P59+P1+KKoCqCriiQRURYGiKBBFHQwGPQRBwM7uDlpaWrC1tYX7p46CrB3Mr6hyu7G2tobZ2dmjF9rxRAKNDQ3Y3t5G/8DA+QWrKt68eYPOzs4z33sgEICiKEerfU1G45ltNyazGUoFPBnPhnjw+3bVv1MmkwmJRH4Ck+uwOxzYDwRQxcCEKG9y3W2i5HGrV7HgUZyTdAeBCRERFRYDEyobjx49grehAc0tzTlfEevxeDAzM4PNjQ0oqorDl8r7fj9mZmeRlGUYjUbY7HY0NTUezZuQzOa0dTz66CO8GR8/mkFxnPdYV0dTUxOaDm6jKgrW1taxt7eL1tbWC1cfv349iurq6tSRDFXFzs7OwZOtJGbnZmG1WKAcBDkqgJGRESwuLkJOJGA0GmE0mqAqSta/X+UsEY8jFotidmYGjY2NMGV43EtV1WsHd/lQ7fFganpa6zKIyl4uu01Ena6sO0wYlpyl0+uRYGBCRFRwDEyofAgC3Afvkuf6nTe7w4Gbt24dffz0yROsrqxgZWUFwyMjV15hfHrAaCYEUURDYwMaGi8+cqEqCmKxKFpaBgFVxeTkJKLRKPQGA+REAsNDQzBLb4/zPH/2DM+fPYPVakVVVRWi0RgCgf0rD4qtFAaDAT09PYhGo3j16hWsVhvq6uuO/u6lIycSWFhchDOLgbH5ZpYkxKLRnIeMRJReLrpNBABimf73yrAkPZ1OBzmRv61LRESUHgMTKgt+vx/mAm6yGRwcxNj4eFZhSb4JooikoiAajWJxcQmKomB4ZOTc23d3d8Pn96PxkgGydEAQ4KlObZVxulyQEwnMzc1dGJjs7OwgHApd+OegpaoqNxYWFtHW3qZ1KUQVIRfdJuW4LYVhCRERFZvyfHuCKs709HRBX/CbzGbcunWr6MKSQ/19fXj+/AWSyST6+vsvvK3VZmNYkiWbzQZXVRUUVUU8FkMwEMDG+jqA1NGdSCQCVVGQSCTgqa5OzT4pQp2dHdja2uQxLKICOwpOjIaKH3DKsORi3JBDRKQN/uSlkheNxRCLxU4cM6l0docD77zzoChnZpSjao8Hz58/h9lsRjgcQXV1NR4/eZLqehKE1Jrfzk6tyzyfIMBoNCIai0Hif0dEBXW62wTI8JhOGc0wYVhyOQYmRETa4E9eKnkmoxEOpxNLi0tobmnWupziwbCkYA7XTwPA6OvXePbsGapcLvT19WF1dQ3V1R4Yi7Qb6ZDBaEQ0EmVgQqSRqx7TKZdNZgxLMsPAhIhIG/zJSyVPEATcGBzEd7/7XUBtYlBAmhq8cePEx5cN6S0WkiQhHAmjClVal0JU0d4e0zn4OE1wEo3FIJb4v3XHjyAxLLlcUpYZmBARaYAzTKhs2Gw2RCIRrcsgKkne+nrsbG9rXQYRHYhAl9qok2a+ye7OTlFu3crU8a4ShiWZYYcJEZE2+JOXyobT6cTi4iIcDgcMBiOMRgOMRiMMRmPRDtskKhZmSUI8Hud6YaIik67jZGtrC729vRpWlR12lVxPOW5GIiIqdgxMqGw0NTXD4XAgdjAENhyJYG9vD7FYDAlZhgCgtrYW1TU1WpdKVJQkiwU+nw9VF6xIJiJtHAYMZqOKvu4uuBz2rNYRa4FBCRERlSoGJlQ2RFGAy+U69+uyLOPJkycMTIjO0eD1YnJyElarFTabDa1tbXxHk6jIrO3uY3V1DXddrqtt1dEAgxIiIip17LumiqGqKkQeNSA6V5XbjQcPHqC7uxvxeByTExNltbqUqBzIsgyDwXg0/+P4nJPTs060crwWzinJEYbXRESa4KtHqhhJRWFgQnQZQYDJbEZPby8URcGzZ8+wtbWldVVEdMBkMiGRiJ/43PFQQqvw5PTjMijJLcYlRETa4JEcqhgGvR6KqmJsdBSCIEA9eOdcURQM3rjBowdEp/QPDCART2BsbBS7u7vo6e7mQFgijRmNJiQS6Y/gnA4oDgfFnrhNjo7vpAtkGJDkCTv9iIg0w8CEKoZOp8O9u3eRTCYBQYDu4IXfzMwM1lZX0dDYqHGFRMXHYDRg5OZNzM/N4+mzZxjo74dksWhdFlHlUVXMzMwgEAgcBf6XOROgIJnTzhMGJIXBlcJERNrhT1+qOKdXDHd0dGB8fBwz0zPo7OzgOWGiNNra2+B2V2F8fByKqsJms6G9vR0mk0nr0ogqQjweh8/nw81bt878O5YpBhylSU4moWNgQkSkCfZWU8UTBAENDQ2IxaJal0JU1BxOJ27fuYO7d+4gHAojKctal0RUMWKxGEwmU9ZhCZWupCzDwMCEiEgTDEyIAExPz8Dj8bC7hCgTggBFSbK7hKiAwuEwjEb+N1eJeCSHiEg7DEyIAPT392Fubg7RSETrUohKQk1NDZaWlrUug6giqIqC+fl5NDY2aF0KaSC1Sro4VkYTEVUaxtVU0RKyjLGxMUTCYfT09MAsSVqXRFQSvF4vJiYmtC6DqCIEQyFIFgusNpvWpZAGgsEQ6uvrtC6DiKgiscOEKlY8Hsejjx7B6XTCYDCktucQUUYEQQQXXRIVhm9vDy6XS+sySCPBYABOp1PrMoiIKhI7TKhiRSIRKEoSOlFEd0+P1uUQlRRBAJDhalMiup7d3T30D/RrXQZpQVWhKApnmBARaYQdJlSxnE4nbt2+jbm5Oa1LISo5kUiE2zqICkFVkUjEYTQata6ENBCNxmCxWLQug4ioYjEwoYo2OTGBzs5OrcsgKjnz8/NoamrSugyishcKhWAwMCypVPuBfbjdbq3LICKqWAxMqGJtbGxAEEXYHQ6tSyEqOUlF4VphogLY8/ngdPLfqUoVDARQVVWldRlERBWLgQlVrOnpadTXceo8UTYECPD7/VA4LJkor3Z3d1FfX691GaSRSCQCq9WqdRlERBWLgQlVrFu3bmNiYgKqomhdClHJaWtrxcLiInw+n9alEJU1OZHgyvsKFYtGYTAYIAiC1qUQEVUsBiZUsSTJDIPBCEHkfwZEV+V0uWA2m3kshyjP+GK5cs3NzaG3t1frMoiIKhpfKVLFGh8fR21tjdZlEJUsOSHDaGRgQpRPoqiDnEhoXQYVmG9vDxaLBXa7XetSiIgqGgMTqkiLi4tIJBKo5QwToqzpdDrE43GtyyAqa0ajEZFoVOsyqIBURcHS0hJ6+/q0LoWIqOIxMKGKs7u7i7W1dbS1t2tdSlZ8Ph+ePX2KcCikdSlU4VwuJ/b2drUug6isGY1GRCIRrcugAlpdXUVLSwsMer3WpRARVTwGJlRxZFlGMikjWmJPQCORCN6Mj2Pf74fH40FClrUuiSqcwWBEkltyiPLKZDIixg6TihGPxeDz+9HU1KR1KUREBIDRNVUURVFgMplw69YtPH36FG1tbbA7HFqXdSE5kcDi0hLkRAIDAwOw2WwIhcN4/uwZBgYGoEvzDlQymcTrV6/QPzAAo9GoQdVUCUKhIJxOp9ZlEJU1o9GI/f19rcugApmfn8dAfz+H/RKVC7MNkGxaV3E5mT9zzsPAhCrKxOQk9v3+o48tVquG1VxMVRSsr69jZ2cHPT09qK6uPvqa1WIq4w5cAABAXUlEQVRBf38/Jicn0XfqiZWqqpiZnoHX68XiwgK6uroAPvGiPIjF4jCbzVqXQVTWDAYDEgl2FFYCv98Po9HIIJqIqIgwMKGKEIvF8ebNOBKJBPoHBo4GVep0Oo0rS293dxcry8toamrGw4cP077T5Ha70dDQgJnpabS1tSGZTGJ9YwP7+/tobmpCS0sLpqamMTo6isamJtisViQSCUgWiwbfEZUjg9GAaDQKB5/cE+VNKjDhcOVyl4gnsLCwgPv37mldChERHcPAhCrC1PQUnC4XqqqqAECzYypKMomNjQ0oipL+BoIAv98Ph92OBw8eQH/JwLfm5mYYDAZMTU1Br9ejtbUVNwYHjwKW7u4utLamgpO52VnodDoMDA5eel2iTDR4vVhcWuK2KaI8stpskGUZoWAQVlsJtHXTlcWiUUxOTmJoaIjHaImIigxfNVHZi8XiCAQCaGlp0bQOn8+HpcVFtLa2wixJgKqe+PrhR22trZAkKePr1tfXo76+/tyvG41GDA4OAEh1rszNzaG7u/vK9ROdZnc4Sm54MlGpEQQBPT09eDMxgTt37mhdDuVYJBzG1PQ0bt68CVsRHxMmIqpUDEyobCUSqfbWzc1NNDU3a1aHLMuYn5uDTq/H/QcPNF0TGAqFYOK7V5RD6qngj4hyz2a3w2G3Y2lxEc0ah/+UO8FgEHNzc7hz5w4kzoMiIipKXCtMZUdVVezu7mJmZgaKoqC3tw8ul0uzejY3NmCz2TAyPKx5WLK8vIxmDcMjKj+CIHC1MFEBdHR2Yn19HXIioXUplAN+vx8L8/O4d/cuwxIioiLGDhMqO9vb25iYnIROp8PgwIDmG2J0Ol1RrIQcHx9HZ2cnBJE5KWUvGAxibHQUOp0OKoBEPI5kMlm0A5SJyoVOp0N7ezvGxsYwPDKidTl0Dbs7O9jY2MS9e/c4U4yIqMjxpzSVndXVVXR3d19pDkg+hSMRdHZ2aV0GZFlOzU4hugYlqcDhcKCvv1/rUogqTnVNDVbX1rCzswOPx6N1OZSFzc1N+PZ8uHvvLnR8A4OIqOjxJzWVFVVVEQqFiyYsAQCP24219TWtyyDKCZPZhFgspnUZRBWrt6cHszOzZwaHU/FbW11FMBDA7du3GZYQEZUI/rSmsqCqKt68eYNHjx7B5XJqXc4JDocDe7u7WpcBPrWmXDCZTFAUBX6fT+tSiCqSyWxGfX0dJientC6FrmB5aQmyLGNkZASiqO1RYSIiyhwDEyoL8/PzUBQF3d3daGpq0rqcFFWF3+/HmzdvYNV4VeDW1hZEQUQ0EoGqKJrWQqWvv38AU1NT/LtEpJGmpiYEAvsIh8Nal0KXUVUsLixAFEUMDAxA0HiuGhERXQ1nmFDJC4XDWF9fx+DgYEYDXpOyjM3NTWxubuLGjRvQ5WHgmm9vD8vLK3C5nBgaHtZ0An4ymcTzFy/gcrmwsbGBSCQCRVGgqiqsVivi8TgAAT093ZoPyKXSYJbMcFVVYXFxEa1tbVqXQ1RxBFFET08P3oyP4/adO1qXQ+dRVczPz8NsNqOnp0fraoiIKAsMTKjkTU9Nob29PaMX+/F4HG/evEF7WxtEUYelpWU0NTVCbzDkpJZQMISFxQU47Hbcu3cXhhxd9zpEUcT3fe/3nqlFVVXs7e0BAHw+PxYWF9Ha2qpFiVSCujo78ejRYxgMBtTU1MJgPPt3PRQMYn5+HvFEAkaDAW6PB16vV4NqicqP3eGA1WbDyvIyGouls5LeUlXMzs7CZrOhq0v7we9ERJQdHsmhkre7t4dYLA4lefHxgGQyicmJCYwMD6OxsRGdnZ2orqnG1NQUpqamEAoGs64hHothanISa+trGB4axsDAQFGEJQAgCELaWgRBgNvthtvtRmNjA7a3ti79PSQ6Igi4efMmIpEInj57ir3dvTM3GX/zBk1NTbg5MoKW1lZsbmxoUChR+ers7MTq6iqSsqx1KXScqmJ6ZgZOp5NhCRFRiWOHCZW8O7dv48WLF6ipqUFDY2P6G6kqJicm0NvbB7vdDgAQRQENXi8avF4EAkHMz89hfn4eNTU1qKmpgZDhBPtgMIi52VkMDg7C5XLl6LsqrKXlZTS3tEDUMUOlzBmMBnR2dUEFkEjEz3xdEAQ4XS4k4gkk4vGi2l5FVA70ej1a29owPj6OG0NDWpdDSHVvTk9Nobq6ml2bRERlgK+OqOTt7OzAbrejoaHhxOe3t7YQDoUAAGtra6ipqYHH4057DbvdhqGhIdy7dw96vR6jo6OYn5tHLBq99PGNRiPMZnPJhiUA0OD1wrfnQzQSwd//3d9pXQ6VGItkwcLCIh4/foyx0dGjz6sHa08fPX6EhYUFuN3p//sjouzV1tQgmVSKYhtbpVMVBZOTk6itrWVYQkRUJthhQiVvfWMDjY2NJ2aYRCMRbGxsQFEUtHd0YM/nw4P79y+9ll6vR1tbG1pbW7G7u4fx8TH09vXBZDK9vZGqYnd3Fzs7O7BYLGhsakI8HoeiKBAz7EopNhaLBSaTEa9HR6HT6bQuh0pMQ2MDGhpTgeWHH3yAsbExJJNJHP4XaTIacev2be0KJCpngoDe3h68evUa9+5VcXi3RlRFwcTEBBobG1PPSYiIqCyU5qs7omPu3b2LtdVV+A4GmB4OWhsaGkL/wABev3qFoRs3rrTKTxAEeDxuDA0NYXFh4cTXZFnG9PQ0urq7EQ6Hsbe7C7vdjp2dnVx+WwU3NDSE7/ve74Pt4MgSUTZGRkbQ2tKC3p4e3D4ISYwmEzbW1zWujKh8mSUJtbU1mJ6e1rqUiqQqCt68eYPm5maGJUREZYaBCZU8vV6P/v5+bG5uAQA2Nzfh8Xhgs9ngrqrCP/pH/whWqzWrazudToiiDsFjA2H1ej2cTicC+/u4ceMGIpEIAoHAiduUqkQiDpHvTtI1mCUJVpsNRpPpaA7QjcFBLC4uHR2RI6Lca2lpgc/vRzQS0bqUihMIBOB0OrkFjIioDDEwobIwNT2N5uYmJOIJbG5uorOz8+hr1z1i0tffh4WFBeBgHkMkGoVer0dTUxNMJhP6+vrw8OHD1GrjEqaqKp4/f4GWFp67ptwSRBH9A/0YGxuDb28PSjKpdUlEZUcQRXR3d2N8fFzrUipOIpHgUGsiojLFGSZUFswmE2ZnZ6EoCgYHB3M6S0Q6GOi6u7sLt8cD394e6uvrc3b9YjE5OQm3uwpWW3bdOEQXsdls6OzsxMrKCqanpyGKOjidDuj1elRVueFwOrQukajkOZ1OSJKEtdVVeE8NQqf8Scgy7Dab1mUQEVEeMDChsjA4OIhkMolgMAin05nz63d3deHDDz+EqNNha2sLHR0dCIXDiMdiqKqqyvnjFdrW9jYCgSB6erq1LoXKWJXbjaqDTTmqomBrawvxeBwTExO4c+c2RJ0O0UgEer0eeoNB42qJSlNXVxeePXuG2ro6DvEuEL1Oh3j87Gp1IiIqfQxMqGzodLq8hCUAjo7gHA6WffLkCQwGA+LxOB4+fJiXxyyUZDKJyYkJDAwOcrsCFYwgiqitqwMAbO/sIB6PwyxJGB0dTf09VFVYrVZIkgS9wQCDXp8KUg5+SZJ0NCOF6JCSVJBIxJFIJJBIJFLbmgTh5C9RhAABgihAPP21o18iBCH19/TwNqXy81FvMKClpQVvxscxeOOG1uVUBIfTicWFBa4SJiIqQwxMiDLU3NyC1dUP0NvbC4PBCFEnYnxsDLIsQ68v3f+UFhYWUFdfz3ciSTPJZDIVQMZiEAQBt+/cAVQVfr8f4XAY8Xgc4XAYSVmGLCcRi8dQ7fGgta1N69Ipj1RFgSzLSCQSiCcSkA9CkMPPJRIJyAfzcAQAEAToRBFGoxFGkwkmoxF6vR6qqkJRVSjJJBRVhaooqc8pKlRVOfq6qiinvn78ayoA9W1tZ4o985mLv7c0nxMEAcLB/+L4/4eQCm8Og5tTH4uiCEEQIYqH/z/1v4FAEKsrK7BYLEfBjyiKR79Of65UAqFiZDQaEY3GoKrqlTbyERFR8SvdV3lEBXb4ZPQwLEnKMnQ6HXx+P6o9Hq3Ly0pSUbC2vo4bfBeSNJSUZYyOjiIWi6Ompjr1SUGA0+WC0+U6c3slmcSzZ88YmJQRJanA7/fB5/MhHA4fvZg3GIwwmYxHIYjVak39/4Nfer2+LF6gqqoKVQVUqICqHnysnvi8qp79mqIoR7+Sh/8/qUBRkujs6kx13MhyKixKc5u3H78dxKy+LepsnUiFU8eDmbfhSyq0EQQRou6gMyeDoKZcwhubzYrd3T14PG6tSyEiohxiYEJ0Ba6qKszMTCMej0On16PaUw2Ho3SHVW5vbcFdVVUWLziodN26dQuCIKQ6tTL4uyjqdNDpdAgFQxxSXKpUFeFwGHt7e/Dv70OAAI/Hjfb2dtjt9or7mZTqFgEOemWK2pmg5iB8ORPeJJPHbpP6/+nDm+SZ+xw91tsHPVsHzg9vDjuNzGYzJEmC6dia83zxer2Ym5tlYEJEVGYYmBBdQWtLC0KhMKqqXDndxKOV1dVVNDU1aV0GVTiD0Xjl+9TV12N9fR2dXZ2X35iKgpxIYG9vDz6fD/F4HHa7HbW1tejp6eGRwBIiCAJ0B6Gl1o6HN8njnTPJJCLRKEKhEDY2NhAOR6CoqSCmsbERrjSda9dlMpuhqirC4QgsFq4YJiIqFwxMiK7AbDbDbDZrXUZOKIqKSCQKU5l8P1RZvPX1ePz4MaB2lGT7fiVQFQX7gQB8e3sIBoMwGIyora3BwMAAJIkvKOn6joc3p/dqpRsCL8syXr58iVAwiMbGxpz/7PB6GzA3N4vBwcGcXpeIiLTDwISoQvn9Ptgddq3LIMqOIMBoMmF/PwCHs3SPxZWbaCSSOmbj90NRFFRVVaGpuRkup7PijtlQ8dHr9bh16xZmZ2cxMTGBru7unHbKOJwOLC4ulPwweCIieos/zYkqVDgchtnE7hIqXY0NDVhbX2NgoqFkMgnfwTGbaDQKi8WC2tpadHR0wGA4/Z4/kfYEQUBnZyecTifGRkfR1d2d046n2ro6LC0tob29PWfXJCIi7TAwIapQkWgUJtPVZ0cQFQtPdTVmZ2cRDARgs7NbqiBUFcFgEHt7e9gPBKDT6VDtqUZ3Tw+sFgu7SKhkVFdXw2q14vnz52hqbk57hCcbNdXVeD06ira2Nv73QERUBjSdWvnee+9heHgYDocDDocDDx8+xF/+5V8efV1VVXzxi19EQ0MDJEnC933f92F0dPTS6371q19Fb28vJElCc3MzfvZnfxbRaDSf3wpRyYlGIjCZTFqXQXQtAwMDmJycxPjYGF6+eAH12IYNyg1VUbC9tYWJN28wOjoKn8+Huro6PLh/H/fv3UNHRztsVitfHFLJkSQJ9+7dw+LCQs5+dgiiCJfTic2trZxcj4iItKVpYNLU1ISvfOUrePz4MR4/fozv//7vx2c+85mjUOS3fuu38Nu//dv43d/9XTx69Aj19fX45Cc/iUAgcO41/+iP/gi/8Au/gC984QsYHx/H1772NfzxH/8xPv/5zxfq2yIqCZFoFEYGJlTirDYbbt+5A091NUwmExYXF7UuqWwEg0HMTM9gbGwMgIDh4WE8fPgQAwMDqK6uLotNYUR6vR7Nzc1YX1/P2TXr671YmJ/P2fWIiEg7mh7J+fSnP33i4y996Ut477338MEHH2BgYABf/epX8Uu/9Ev45//8nwMA/uAP/gB1dXX4H//jf+AnfuIn0l7zu9/9Lj7+8Y/jR3/0RwEAbW1t+Jf/8l/io48+yu83Q1RikrJcFGshiXKhtrYWtTU1ePToMerr67n9KUuJeAKbW5vY292Fw+FAZ2cHHA7OiKHy1tzcjO9+97uora2FLgfDWg1GA3Q6HQKBIOx2Ww4qJCIirRTN20PJZBJf//rXEQqF8PDhQ8zNzWF9fR3/+B//46PbmEwmfO/3fi/+/u///tzrfOITn8CTJ0+OApLZ2Vn8xV/8Bf7JP/kn594nFothf3//xC8iIioxgoD29jZMT09rXUlJURUFOzs7eDM+jtnZGbicTrzzzju4ceMGwxKqCIIgoKurC0tLyzm7ZkNDI2ZnZ3J2PSIi0obmQ19fvXqFhw8fIhqNwmaz4Zvf/CYGBgaOQpG6uroTt6+rq8PCwsK51/uRH/kRbG1t4ROf+ARUVYUsy/jJn/xJ/MIv/MK59/nyl7+MX/3VX83NN0RUAlRVhap1EUR5UF1Tg6XlZfh9PjhdLq3LKWrhUAgbGxsIhUKora3FyMgI5xpRxaqtrcXc3Dxi0WhOOtSsNisiCxHE43EYjRywTkRUqjTvMOnt7cXz58/xwQcf4Cd/8ifx2c9+9uC8dMrpIXKqql44WO7b3/42vvSlL+G//tf/iqdPn+LP/uzP8H/+z//Br/3ar517n89//vPw+/1Hv5aWlq7/jREVsXg8DiNXflKZ6u/rw/TMDKAyFjxNlmWsra7i9avX2NjcREtrKx4+fIiuri6GJVTx+vp6sbCQuzlIdfX1Ob0eEREVnuaBidFoRFdXF+7evYsvf/nLGBkZwe/8zu+gvr4eAM4M4drc3DzTdXLcr/zKr+Ddd9/Fj//4j2NoaAj/7J/9M/zGb/wGvvzlL0M5ZwK6yWQ62tRz+IuonEWjUb7jRWXLLEkwm0zY2t7WupTioKrY293DxMQEpqemYLPZ8ODBfYwMD6PK5eJ2G6IDTqcTer0OwWAwJ9fzuN3Y3NyAojC8JaLy89d//df49Kc/jYaGBgiCgP/1v/7Xpff5oz/6I4yMjMBiscDr9eJzn/scdnZ28l/sNWgemJymqipisRja29tRX1+Pb33rW0dfi8fj+M53voOPfexj594/HA6fmdyv0+lSRxD4biMRACASiTAwobLW09OLhfn5iv65H41EsDA/j9evXyMWi+LG4CDu37+PxsZGDnwmOkdvXx8WFxZy06EmCPB4PFhbX7v+tYiIikwoFMLIyAh+93d/N6Pb/+3f/i1+7Md+DP/m3/wbjI6O4hvf+AYePXqEH//xH89zpdej6QyTX/zFX8SnPvUpNDc3IxAI4Otf/zq+/e1v4/3334cgCPiZn/kZ/MZv/Aa6u7vR3d2N3/iN34DFYjnagAMAP/ZjP4bGxkZ8+ctfBpDavPPbv/3buHXrFh48eIDp6Wn8yq/8Cn7wB3+QTxCJDkS5UpjKnMFogM1mw8bGxlHHYiVIJpPY3trC9vY2JElCc3ML3O4qdpEQZUgym+F0OrG7twe3233t69XV1WFiYgKNDQ05qI6IqHh86lOfwqc+9amMb//BBx+gra0NP/3TPw0AaG9vx0/8xE/gt37rt/JVYk5oGphsbGzg3XffxdraGpxOJ4aHh/H+++/jk5/8JADgP/2n/4RIJIKf+qmfwt7eHh48eIC/+qu/gt1uP7rG4uLiiY6SX/7lX4YgCPjlX/5lrKysoKamBp/+9KfxpS99qeDfH1GxEnU6JOJxrcsgyqvunh48ffIUdbW1EMSia6jMHVWF3+/H5uYmErKMhoYG3Lt3D/ocrEclqkTd3d348MMPU0fWrvmzQ6fXw2w2w+fzwcVB1ERU5E5vizWZTDmbcfaxj30Mv/RLv4S/+Iu/wKc+9Slsbm7iT/7kTy7cZlsMBLWS+5XPsb+/D6fTCf/aIueZUFna3t7G1tYWGpuatC6FKK+mJidhsVjK8u96LBrFxuYm/H4/3G43WpqbYbVatS6LqCzMzc8jEY+j3uu99rWikQiWl5dx+/btHFRGZcHi1LqConD0msvvL7vXXIffm2/6FRzH3uwvVvuBAFxdQ2c+/4UvfAFf/OIXL72/IAj45je/iX/6T//phbf7kz/5E3zuc59DNBqFLMv4wR/8QfzJn/wJDEW8jIJvPxFVAEVRMDMzAyCVFIfC4Yqe7UCVo6urC48fP4bX64VYJscyg4EAVlZWIAgiWltbMDgwwCM3RDnW2tKK737wXdTU1l7rSLeqKNjb20MsFsthdURE+bG0tHQivMrlBr2xsTH89E//NP7zf/7P+IEf+AGsra3h53/+5/Hv/t2/w9e+9rWcPU6uMTAhKnOqquLZ8+dw2O2QJAnxeAImownV1R6tSyPKO0EU4fF4sLy8jJbWVq3LyZ6qYm/Ph7W1VVgsFgwMDsJqsWhdFVHZEkUBnR0dWF5eRms2PztUFdvb21hbW0NzczPeeeed3BdJRJRj+dwY++Uvfxkf//jH8fM///MAgOHhYVitVnzP93wPfv3Xfx3eHHT05QMDE6Iyt7G5CQFAXQkPvozHYlhcXET81NwVVVVhNktwuZxwu93lPaeCstbR0YFHjx6jobGx5OZ6qIqCre1tbG5swO324Nat2zCZuOGKqBDq6uowv7CAeCx2pUHpfr8fS4tLqK6pxjvvvMOlA0RESG2zPf087PDnYzF3vpfWM0ciurK62loszC8gHArBUmLzDZRkEsvLywiGQujv64PTefLMr6qqCIVCWF1bw8LCAtra2zWqlIqaIKCurhaLi4vo6OjQupqMJJNJbKyvY2dnBw0NDXjw4AFfdBEVmCAI6OvtxczsLLq7uy+9fTgUwsLCIiwWCXfv3oHRyHCTiMpXMBjE9PT00cdzc3N4/vx5aq5aSws+//nPY2VlBX/4h38IILXN9t/+23+L99577+hIzs/8zM/g/v37aCjiTWIMTIjKnCAIuHlzBI8fP8bA4GBJveianZ1FXX09BgcH085oEAQBNpsNPd3d+ODDD5GIx2HgE1RKo6WlBY8ePUZzU1NR/x1JxONYXV1FMBhES0sLenp6IYqcT0KkFZfLBQG48E2HeCyGhcVFAMCNoRs8LkdEFeHx48f4h//wHx59/HM/93MAgM9+9rP4/d//faytrWHx4GcjAPyrf/WvEAgE8Lu/+7v4j//xP8LlcuH7v//78Zu/+ZsFr/0quCUnDW7JoXK0tbWFxcVFdPf0aF1KWhvr66g9vv5VVfH69Sg+9rGHGd1/d3c31UHQ2ZnHKqmUra6sIBQKFeV/A5FIBCvLy5BlGe0dHaj2eDjIlahIhMMRvHr1Ev0DAyc+n5RlLC8vIxwOo7e3l2uDKXPckgOAW3KKyeGWnHL8s7gudpgQVYiamhps7+xgdWUV1dUeGAyGS2d+hEMhbG1tQa/Xn/hls9mgy8EsCFVRsB8IIBGPY3ZuDgaDAW7P22G0oi7zmSRutxvT09OIxWI5nehN5aOhsRGPHz1CLBqFyWzWuhwAQGB/HysrK9Dr9ejs7Dxz7IyItGexSLDb7djb3UOVuwqqomBtbQ27u7vo7OxMhf0MOImIyhIDE6IK0tfbi6npaSyvrCAeiyGpKKj2eI7eFZNlGatra9Dr9XA5nVhdXUVfXx+SySQSiQTi8Tii0SgWFxfR1NSEKrf7WvVsbG4iFAzBarXgnXfewejr13C5qlJBiSBceQBUT08P5ubm0NnVda26qHy1tLRgbm4Off392hWhqtjd3cXa2hpsdjtu3BiCxSJpVw8RXaq7uxsfPXoEWU5gfX0dzc3N6O3tZVBCRFTmGJgQVRBBENBzbHCdLMtYW1vD6uoqIAjQ6XTo7uqCz+fD7u4u7t+/n3bmSXt7OyYnJ7G2tgZVVWGz2dDa1pb6oqoiFo9jfX0dqqKcO4hVVRTsbG9jZGQEloPz3p1dXZiZmU4N1xMECIIARVEgZrj9xuVyIZlM4vWrV2hta4O9BFogqbBq6+qwtLSESDgMqcBzBlRFwebmFjY3N1BTU4M7dzgUkqhUGAwGtLS0IBqJcvMNEVEFYWBCVMH0ej2am5vR3Nx84vMOhwMtLS0X3m/g4Cy3qqr48MMPsbmxga2tLQiiCLPJBE91NVaWl8/cNxwKwe/3Y2dnB21tbUdhCZDa6OP3+bC2tgZvQwPMJhPC4TBsNlvG31P/wADCoRAmJycxNDQE8N0/OqWzsxNTU1MYHhkpzAOqKjY3N7GxsYHGpia+2CIqUc1NTVqXQEREBcbAhIiuRRAExONxRCIRvPPOOyfak30+HxYWFtDa0oJAMIilpSVYJAk1NbVoa2uHyXT23fXu7m48efIEkZkZ7O/vIxqNXikwsVossFosWF5eRiKRKOqNKKQNV1UVFhcXsbu7C/c1j5VdJrC/j4WFRdTW1eLhw4cZd0sRERERkfYYmBDRtd2+cwdWi+XMWe6hGzcwNzeHZ8+ewVVVhZGREUiXDNsUBAG3bt1CKBSCyWTK+siC1WpFJBJhYEJp9fX14eXLl6hyuS4dfpyNeDyO+bl56PU63Llzm4OIiYiIiEoQAxMiujab1Xru19rb29Ha2nqld9Z1Ot21V5o5HA7s7u7Cwa0jlIbRZILT6cTy8gqaW5ovv0OGVEXBysoK9vf30dfXD5eLf/+IiIiIShV7g4ko77Q4hlBXV5c60hOJFvyxqTR0dXVhfX0diXj8+hdTVezs7OD169dwOp148OABwxIiIiKiEsfAhIjKkiAIuHHjBhYWF7QuhYqUIIpobm7CzMzMta4TDoUwNjaGWDS1PaOxsZGrRomIiIjKAI/kEFHZstlsSMoyVEXJy5wKKn31Xi9WV1cRDARgu+IaajmRwOLiIpLJJIaHR2CxSHmqkoiIiIi0wFcQRFTWqmtqsLe3p3UZVMR6ensxNTUFqGpGt1dVFWurq3jz5g2amptx584dhiVEREREZYiBCRGVtbraOvh8Pq3LoCJms9lgNBqxubl56W19Ph9ev34Nk8mEhw8fotrjKUCFRERERKQFHskhorJms1kRiXLwK12sr68PT58+g6e6Gjqd7szXY9Eo5ubnIUkS7t27B6PBoEGVRERERFRIDEyIqKxx+CZlQqfXo6amBgvz8+jo7Dz6fDKZxPLSEiKRCPr7+2G/4pwTIiIiIipdPJJDRGXPZDTmZnUslbW2tlbs7u6mVlGrKjY3NzE2Ooqamhrcu3ePYQkRERFRhWGHCRGVPYfDgVA4DJfRqHUpVMwEAR0dHXjzZhwGgwE1NTV45+FD6LhhiYiIiKgiMTAhorLndrsxNTUFu92edj4FEQAk4nHs7O7CaDTi5q1bMJtMWpdERERERBpiYEJEZc/tdqOzsxNjo6Po6OiA1WbTuiQqIqqiYHV1FT6fD339/ahyubQuiYiIiIiKAAMTIqoI1dXVsNvtePHyJUxGI7zeBpgls9Zlkcb2dnexvLyMlpYW9PX1cUgwERERER1hYEJEFcNkMuHe3bvY29vD3Pw8krKMxqYmDvOsQJFIBPNzc7Db7Xjw4AH0ev5zSEREREQn8RkiEVUUQRDgdrvhdrsRjUbx4sULVFdXo6amBmB3QdlLyjIWFxcRTyRw48YNWK1WrUsiIiIioiLF0f9EVLHMZjPu3r0LRVHw+vVr7O3uaV0S5YuqYmN9HWPj4/B6vbh39y7DEiIiIiK6EAMTIqpoOp0O3d3duHvvHkKhIEZHRxHY39e6LMqhwP4+Xr9+DVEU8fCdd1BbW6t1SURERERUAngkh4gIgNFgwMDAAKKxGCYnJrGysoLm5hZYbexCKFXxeBwL8/PQ6/W4c+cOTFwTTERERERXwMCEiOgYs8mE4eEhhMNhvHr1GvX19ahyV2ldFl2BqihYWVnB/v4++vr64XI5tS6JiIiIiEoQj+QQEaVhsVhw9+4dLK8sIx6Pa10OZWh3ZwevX7+Gw+HEgwcPGJYQERERUdYYmBARnUOn02Hoxg1MTU0Bqqp1OXSBcDiMsbExRCIRPHjwAE1NjRC49YiIiIiIroFHcoiILuBwOOD1erG0tITmlhaty6FTZFnG4sICZFnG8NAQLBaL1iURERERUZlghwkR0SVaW1ogyzI21te1LoUOqSrW19bwZnwcjU1NuHv3LsMSIiIiIsopBiZERJcQBAHDw8MIBkPYWN9IfZJHdDSz7/fj9evX0OsNeOedh6iprta6JCIiIiIqQzySQ0SUAUEQcPPmCJ4/f47wbAg7Ozvo7x/g2uECisVimJ+fh8loxN27d2E0GrUuiYiIiIjKGAMTIqIMpUKTm5iZmUFXVxdevHiB1rY22O12rUsra0pSwcrKMgLBIPr7++F0OLQuiYiIiIgqAAMTIqIrEAQBXV1dAABJkhCPxQAGJvmhqtjZ2cHq6ira29sxMDDAzTdEREREVDAMTIiIsqCqKkKhEDoPwhPKLTmRwPT0NJxOJ9555x3odDqtSyIiIiKiCsPAhIgoC4IgwOlyIRgMwmazaV1OWdn3+7GwsIDBwUG4XC6tyyEiIiKiCsXAhIgoSzXV1dje3mZgkiuqiqWlJURjMdx/8AAGPf+JIiIiohJmMANGs9ZVXM6Q0LqCosW1wkREWVpfX0dVVZXWZZSFeDyO0bEx2Gw23L51i2EJEREREWmOgQkRUZY6O7uwurqqdRklb293F5MTExgcHERraysHuxIRERFRUeBbeEREWdLpRCiKonUZJUtVFCwsLEBVVTx48ICDXYmIiIioqDAwISLK0tjYOFrb2rQuoyTFolFMTU2jtbUFjY2NWpdDRERERHQGAxMioizJcgIWi0XrMkrO9tYWNjY2MDIyDKvVqnU5RERERERpMTAhIsoWZ21cjapiYWEBgiDgwYMHEEWO0SIiIiKi4sXAhIjoilRVxfzCgtZllJaDsMRkMqGnp0fraoiIiIiILsXAhIjoimZmZhAOhzHQ3691KaWBYQkRERERlSD2QxMRXZHH44GqqjySkwmGJURERERUohiYEBFdkWSxIBgMal1G8WNYQkREREQljIEJEdEVJJNJPHv6FJ2dnVqXUtwYlhARERFRiWNgQkR0BcFgEBaLBRauwz0fwxIiIiIiKgMMTIiIrsBmtyMSiWhdRvFiWEJEREREZYKBCRHRFehEEU6nE9NTU1qXUpQuC0sSiQTiiUSBqyIiIiIiujoGJkREV9Tf349YPK51GUVnYX7+0s6S2dlZfOuv/gpzc3MFrIyIiIiI6OoYmBARZUlmp8SRTMISAJAkCUNDQ2hraytMYUREREREWWJgQkSUha7OTrx69QpxdppkHJYAQEtLC1paWiAIQgEqIyIiIiLKHgMTIqIs1NTUwOFwoNJf9l8lLCEiIiIiKiUMTIiIspRMJrUuQVMMS4iIiIionDEwISLKUldXF+bm5rUuQxML8/MwMiwhIiIiojLGwISIKEsulwuyXHmDXw/Dkl6GJURERERUxhiYEBFdhyAAqqp1FQXDsISIiIiIKoVe6wKIiEqZ0WBAIiHDYDRoXUre5TssSSQS2NnZgd5ggEGvh16vhyRJEEVm+0RERERUeAxMiIiuwWq1IhqNlH1gsrCwkPfOkr29PSwuLsJut0NOJpGUZUSiUdy/dw86nS5vj0tERERElA7ftiMiugaL1YpINKp1GXm1sLAAo9GY92M4drsdyWQSyWQSjQ0N6OjshNPhwOjoaF4fl4iIiIgoHXaYEBFdg9Vqxb7fr3UZeVOosAQAJEnCO++8g82tLUxMTACCAIskoa+vL++PTURERER0GgMTIqJrsFosiJZph0khw5JDgiCgrrYWtTU1iMcTMJmMBXtsIiIiIqLjeCSHiOgajEYj4onyWy2sRVhynCAIDEuIiIiISFMMTIiIrkEQBAhaF5FjWoclh1RVhaIomtZARERERJWLgQkR0TWJogi1TF7YF0tYAgCBQAB/+f77WFpa0roUIiIiIqpADEyIiK5JkqSSn2OiJBXMz80XTVgCAEaTCd76ejQ0NGhdChERERFVIA59JSK6JqvVhmg0CrMkISnLkGUZCVmGnEhAPvhYlpNQlCT0BgMMej0MBsOJX6JOp0ntoWAQ6+vriMZiaG5qQlNTkyZ1pGM2mXD79m2tyyAiIiKiCsXAhIjomqqqXBgff4P19Q0YDIdhiBFGowFGkwk2my0Viog6JBJxxONxxGJxRCL7qf8fjyOZTAKqCgBQAeh1OhgMBugPwhX9YbiiN8BgTIUugnj1JsFEPIFAYB+BYBCB/QDsdhs6OzvhcDhy/LtCRERERFTaGJgQEV1TVVUVPvaxhzm7nqqqSCaTiMfjR79isRhi8TiCgcDR59SDgOXoficvcvZrqgqjyYQqVxWamprgdDggZhG6EBHR/7+9Ow+uqr7/P/66JLkJIQsmQBbWIHwhyCImyoRlYGoJq4JVKQIGBqcjbYAsUsNiheJIBJSxJSzGssxUHWiFYKwBicUGECwQiI2YGhxDYiUxBbMJkvX8/vDHLfckgRBuckl4Pmb4457zOZ/z+dz3XOZzXznnXADA3YDABADuMBaLRa6urnJ1dZWnp6ezhwMAAADclfjTIgAAAAAAgAmBCQAAAAAAgAmBCQAAAAAAgAmBCQAAAAAAgAmBCQAAAAAAgAmBCQAAAAAAgAmBCQAAAAAAgImrswcAAAAAAEB7Y7G6y2L1cPYwbspirXL2EO5YXGECAAAAAABgQmACAAAAAABgQmACAAAAAABgQmACAAAAAABgQmACAAAAAABgQmACAAAAAABgQmACAAAAAABgQmACAAAAAABgQmACAAAAAABgQmACAAAAAABgQmACAAAAAABgQmACAAAAAABgQmACAAAAAABgQmACAAAAAABgQmACAAAAAABgQmACAAAAAABgQmACAAAAAABgQmACAAAAAABgQmACAAAAAABgQmACAAAAAABgQmACAAAAAABgQmACAAAAAABgQmACAAAAAABgQmACAAAAAABgQmACAAAAAABgQmACAAAAAABgQmACAAAAAABgQmACAAAAAABgQmACAAAAAABgQmACAAAAAABgQmACAAAAAABgQmACAAAAAABgQmACAAAAAABgQmACAAAAAABgQmACAAAAAABgQmACAAAAAABgQmACAAAAAABg4tTAZMuWLRo6dKh8fHzk4+OjiIgI7d+/37bfMAytWrVKwcHB6tixo8aNG6ezZ8/etN/S0lJFR0crKChIHh4eCg0NVVpaWktOBQAAAACAu8bmzZsVEhIiDw8PhYWF6ciRIzdsn5GRobCwMHl4eKhv377aunVrK420+ZwamPTo0UOvvPKKTp06pVOnTulnP/uZpk2bZgtF1q1bpw0bNigpKUknT55UYGCgxo8fr4qKikb7rKqq0vjx43X+/Hm9++67+vLLL/Xmm2+qe/furTUtAAAAAADard27dys2NlYrVqzQmTNnNGbMGE2aNEkFBQUNts/Ly9PkyZM1ZswYnTlzRsuXL9fixYu1Z8+eVh75rbEYhmE4exDX8/Pz0/r16zV//nwFBwcrNjZWCQkJkqTKykoFBARo7dq1evbZZxs8fuvWrVq/fr3+/e9/y83NrVljKC8vl6+vr8oKC+Tj49PsuQAAAACAHU9fZ4/gjmD7zlVW1u6+c7W175Pl5eXyDep1S7UYMWKEHnjgAW3ZssW2LTQ0VNOnT1diYmK99gkJCUpNTVVOTo5t24IFC/TZZ5/p+PHjtz+JFuLq7AFcU1tbq7/+9a+6fPmyIiIilJeXp6KiIkVGRtrauLu7a+zYsTp27FijgUlqaqoiIiIUHR2t9957T127dtWsWbOUkJAgFxeXBo+prKxUZWWl7XVZWZkkqfwGV7IAAAAAwC2rsTh7BHeE8vJyST89hqG9aivfJ6+N81pNrnF3d5e7u3u99lVVVcrMzNTSpUvttkdGRurYsWMNnuP48eN23+0lacKECdq2bZuqq6ubfbFDS3N6YJKdna2IiAhdvXpVXl5eSklJ0aBBg2xvdEBAgF37gIAA5efnN9rf119/rUOHDmn27NlKS0vTuXPnFB0drZqaGr344osNHpOYmKjf//739bb3/L/7bmNmAAAAAIAbqaiokK9v+7rqxmq1KjAwsE19n/Ty8lLPnj3ttq1cuVKrVq2q1/bixYuqra1t8Lt6UVFRg/0XFRU12L6mpkYXL15UUFDQ7U2ghTg9MBkwYICysrJUWlqqPXv2aO7cucrIyLDtt1jsE1jDMOptu15dXZ26deum5ORkubi4KCwsTBcuXND69esbDUyWLVum+Ph42+vS0lL17t1bBQUF7e7DezcpLy9Xz5499c0337SJS+FQHzVs+6hh20cN2wfq2PZRw7aPGtozDEMVFRUKDg529lAczsPDQ3l5eaqqqnL2UJqsoe/ZDV1dcr1b/a7eUPuGtt9JnB6YWK1W9evXT5IUHh6ukydP6g9/+IPtuSVFRUV2aVNxcXG9ZOp6QUFBcnNzs7v9JjQ0VEVFRaqqqpLVaq13TGOXGvn6+vKfWTtw7VeY0HZRw7aPGrZ91LB9oI5tHzVs+6jh/7TnP057eHjIw8PD2cNoEV26dJGLi0u9q0lu9F09MDCwwfaurq7y9/dvsbHeLqf+Sk5DDMNQZWWlQkJCFBgYqPT0dNu+qqoqZWRkaOTIkY0eP2rUKH311Veqq6uzbcvNzVVQUFCDYQkAAAAAAGgaq9WqsLAwu+/qkpSent7od/WIiIh67Q8ePKjw8PA79vklkpMDk+XLl+vIkSM6f/68srOztWLFCv3jH//Q7NmzZbFYFBsbqzVr1iglJUWff/655s2bJ09PT82aNcvWR1RUlJYtW2Z7/etf/1qXLl1STEyMcnNz9cEHH2jNmjWKjo52xhQBAAAAAGhX4uPj9ac//Unbt29XTk6O4uLiVFBQoAULFkj66bEXUVFRtvYLFixQfn6+4uPjlZOTo+3bt2vbtm1asmSJs6bQJE69Jee7777T008/rcLCQvn6+mro0KE6cOCAxo8fL0l6/vnn9eOPP+o3v/mNSkpKNGLECB08eFDe3t62PgoKCtShw/9yn549e+rgwYOKi4vT0KFD1b17d8XExNhu8WkKd3d3rVy58qb3bOHORh3bPmrY9lHDto8atg/Use2jhm0fNUR78stf/lKXLl3S6tWrVVhYqMGDBystLU29e/eWJBUWFqqgoMDWPiQkRGlpaYqLi9OmTZsUHBysP/7xj3r88cedNYUmsRjt+XecAAAAAAAAmuGOe4YJAAAAAACAsxGYAAAAAAAAmBCYAAAAAAAAmBCYAAAAAAAAmLS7wOTw4cN65JFHFBwcLIvFon379tn2VVdXKyEhQUOGDFGnTp0UHBysqKgoXbhwwa6PyspKLVq0SF26dFGnTp306KOP6j//+c8Nz1tTU6MXXnhBISEh6tixo/r27avVq1errq6uJabZrjmihsnJyRo3bpx8fHxksVhUWlrapHNv3rxZISEh8vDwUFhYmI4cOeLAmd09nFXDxMREPfjgg/L29la3bt00ffp0ffnllw6e3d3DmZ/FaxITE20/M49b58wafvvtt5ozZ478/f3l6emp+++/X5mZmQ6c3d3BWTVkXeM4t1vD77//XosWLdKAAQPk6empXr16afHixSorK7vpuVnXOI6z6sjaBnCudheYXL58WcOGDVNSUlK9fVeuXNHp06f1u9/9TqdPn9bevXuVm5urRx991K5dbGysUlJStGvXLh09elQ//PCDpk6dqtra2kbPu3btWm3dulVJSUnKycnRunXrtH79em3cuNHhc2zvHFHDK1euaOLEiVq+fHmTz7t7927FxsZqxYoVOnPmjMaMGaNJkybZ/RwWmsZZNczIyFB0dLQ+/fRTpaenq6amRpGRkbp8+fJtz+lu5Kw6XnPy5EklJydr6NChzZ7D3c5ZNSwpKdGoUaPk5uam/fv364svvtBrr72mzp073+6U7jrOqiHrGse53RpeuHBBFy5c0Kuvvqrs7Gzt3LlTBw4c0DPPPHPD87KucSxn1ZG1DeBkRjsmyUhJSblhmxMnThiSjPz8fMMwDKO0tNRwc3Mzdu3aZWvz7bffGh06dDAOHDjQaD9Tpkwx5s+fb7ftF7/4hTFnzpzmTwDNquH1Pv74Y0OSUVJSctNzPfTQQ8aCBQvstg0cONBYunTprQwZJq1ZQ7Pi4mJDkpGRkXHLx8Jea9exoqLC6N+/v5Genm6MHTvWiImJufVBw05r1jAhIcEYPXp0M0eKxrRmDVnXtIzbreE1f/nLXwyr1WpUV1c32oZ1TctpzTqasbYBWle7u8LkVpWVlclisdj+6pWZmanq6mpFRkba2gQHB2vw4ME6duxYo/2MHj1af//735WbmytJ+uyzz3T06FFNnjy5RceP+jVsjqqqKmVmZtrVXZIiIyNvWHc4hiNq2Fi/kuTn5+fQftEwR9YxOjpaU6ZM0c9//vPbHxiazFE1TE1NVXh4uJ588kl169ZNw4cP15tvvumYQeKGHFVD1jXO05QalpWVycfHR66urg3uZ13jfI6oY2PHSKxtgNbS9E9nO3T16lUtXbpUs2bNko+PjySpqKhIVqtV99xzj13bgIAAFRUVNdpXQkKCysrKNHDgQLm4uKi2tlYvv/yynnrqqRadw92uoRo2x8WLF1VbW6uAgAC77TerO26fo2poZhiG4uPjNXr0aA0ePNhh/aJhjqzjrl27dPr0aZ08edJBo0NTOLKGX3/9tbZs2aL4+HgtX75cJ06c0OLFi+Xu7q6oqCgHjRhmjqwh6xrnaEoNL126pJdeeknPPvtso/2wrnEuR9XRjLUN0Pru2sCkurpaM2fOVF1dnTZv3nzT9oZhyGKxNLp/9+7deuutt/TOO+/ovvvuU1ZWlmJjYxUcHKy5c+c6cuj4/261hk1hrvHN6o7b0xI1vGbhwoX617/+paNHjzq0X9TnyDp+8803iomJ0cGDB+Xh4eGgEeJmHP1ZrKurU3h4uNasWSNJGj58uM6ePastW7YQmLQQR9eQdU3ra0oNy8vLNWXKFA0aNEgrV668aZ+sa1pfS9TxGtY2QOu7KwOT6upqzZgxQ3l5eTp06JBd8hsYGKiqqiqVlJTYXWVSXFyskSNHNtrnb3/7Wy1dulQzZ86UJA0ZMkT5+flKTExkYdECblTD5ujSpYtcXFzq/dWluLi43l9n4BiOruH1Fi1apNTUVB0+fFg9evRwWL+oz9F1zMzMVHFxscLCwmzbamtrdfjwYSUlJamyslIuLi63O2xcpyU+i0FBQRo0aJDdttDQUO3Zs+e2+0Z9LVFD1jWtqyk1rKio0MSJE+Xl5aWUlBS5ubk12h/rGudwdB2vx9oGcI677hkm1/4jO3funD766CP5+/vb7Q8LC5Obm5vS09Nt2woLC/X555/fMDC5cuWKOnSwfztdXFz4+b0WcLMaNofValVYWJhd3SUpPT39hnVH87REDaWf/nK2cOFC7d27V4cOHVJISIhD+kXDWqKODz/8sLKzs5WVlWX7Fx4ertmzZysrK4uwxMFa6rM4atSoej97mZubq969ezukf/xPS9WQdU3raUoNy8vLFRkZKavVqtTU1Jtegce6pvW1RB0l1jaAs7W7K0x++OEHffXVV7bXeXl5ysrKkp+fn4KDg/XEE0/o9OnT+tvf/qba2lpb8u7n5yer1SpfX18988wzeu655+Tv7y8/Pz8tWbJEQ4YMsXv44MMPP6zHHntMCxculCQ98sgjevnll9WrVy/dd999OnPmjDZs2KD58+e37hvQDtxuDaWfnkVTVFRk6yc7O1ve3t7q1auX7SFZ5hrGx8fr6aefVnh4uCIiIpScnKyCggItWLCgNaffLjirhtHR0XrnnXf03nvvydvb29avr6+vOnbs2Grzby+cUUdvb+9692V36tRJ/v7+3K/dDM76LMbFxWnkyJFas2aNZsyYoRMnTig5OVnJycmtOf12wVk1ZF3jOLdbw4qKCkVGRurKlSt66623VF5ervLycklS165dbUEy65qW5aw6srYBnMyJv9DTIq79ZJ7539y5c428vLwG90kyPv74Y1sfP/74o7Fw4ULDz8/P6NixozF16lSjoKDA7jy9e/c2Vq5caXtdXl5uxMTEGL169TI8PDyMvn37GitWrDAqKytbaebthyNquHLlygbb7Nixw9bGXEPDMIxNmzYZvXv3NqxWq/HAAw/wk23N5KwaNtbv9ceg6Zz5WbwePyvcfM6s4fvvv28MHjzYcHd3NwYOHGgkJye3zqTbGWfVkHWN49xuDRs7XpKRl5dnOw/rmpblrDqytgGcy2IYhiEAAAAAAADY3HXPMAEAAAAAALgZAhMAAAAAAAATAhMAAAAAAAATAhMAAAAAAAATAhMAAAAAAAATAhMAAAAAAAATAhMAAAAAAAATAhMAABxk3Lhxio2NbTfnnDdvnqZPn94ifQMAANzpXJ09AAAA0Hx79+6Vm5ub7XWfPn0UGxvb6sENAABAe0NgAgBAG+bn5+fsIQAAALRL3JIDAEALKCkpUVRUlO655x55enpq0qRJOnfunG3/zp071blzZ3344YcKDQ2Vl5eXJk6cqMLCQlubmpoaLV68WJ07d5a/v78SEhI0d+5cu9tkrr8lZ9y4ccrPz1dcXJwsFossFoskadWqVbr//vvtxvf666+rT58+tte1tbWKj4+3nev555+XYRh2xxiGoXXr1qlv377q2LGjhg0bpnfffdcxbxgAAMAdhsAEAIAWMG/ePJ06dUqpqak6fvy4DMPQ5MmTVV1dbWtz5coVvfrqq/rzn/+sw4cPq6CgQEuWLLHtX7t2rd5++23t2LFDn3zyicrLy7Vv375Gz7l371716NFDq1evVmFhoV34cjOvvfaatm/frm3btuno0aP6/vvvlZKSYtfmhRde0I4dO7RlyxadPXtWcXFxmjNnjjIyMpr+xgAAALQR3JIDAICDnTt3Tqmpqfrkk080cuRISdLbb7+tnj17at++fXryySclSdXV1dq6davuvfdeSdLChQu1evVqWz8bN27UsmXL9Nhjj0mSkpKSlJaW1uh5/fz85OLiIm9vbwUGBt7SmF9//XUtW7ZMjz/+uCRp69at+vDDD237L1++rA0bNujQoUOKiIiQJPXt21dHjx7VG2+8obFjx97S+QAAAO50BCYAADhYTk6OXF1dNWLECNs2f39/DRgwQDk5ObZtnp6etrBEkoKCglRcXCxJKisr03fffaeHHnrItt/FxUVhYWGqq6tz6HjLyspUWFhoC0IkydXVVeHh4bbbcr744gtdvXpV48ePtzu2qqpKw4cPd+h4AAAA7gQEJgAAOJj52R/Xb7/2XBFJdr9uI0kWi6Xesde3v1HfN9KhQ4d6x11/a1BTXAtpPvjgA3Xv3t1un7u7+y2PCQAA4E7HM0wAAHCwQYMGqaamRv/85z9t2y5duqTc3FyFhoY2qQ9fX18FBAToxIkTtm21tbU6c+bMDY+zWq2qra2129a1a1cVFRXZhSZZWVl25woKCtKnn35q21ZTU6PMzEy7Obm7u6ugoED9+vWz+9ezZ88mzQkAAKAt4QoTAAAcrH///po2bZp+9atf6Y033pC3t7eWLl2q7t27a9q0aU3uZ9GiRUpMTFS/fv00cOBAbdy4USUlJfWuOrlenz59dPjwYc2cOVPu7u7q0qWLxo0bp//+979at26dnnjiCR04cED79++Xj4+P7biYmBi98sor6t+/v0JDQ7VhwwaVlpba9nt7e2vJkiWKi4tTXV2dRo8erfLych07dkxeXl6aO3dus94rAACAOxVXmAAA0AJ27NihsLAwTZ06VRERETIMQ2lpafVuw7mRhIQEPfXUU4qKilJERIS8vLw0YcIEeXh4NHrM6tWrdf78ed17773q2rWrJCk0NFSbN2/Wpk2bNGzYMJ04ccLu13gk6bnnnlNUVJTmzZuniIgIeXt72x42e81LL72kF198UYmJiQoNDdWECRP0/vvvKyQk5BbeGQAAgLbBYjTnZmgAANDq6urqFBoaqhkzZuill15y9nAAAADaNW7JAQDgDpWfn6+DBw9q7NixqqysVFJSkvLy8jRr1ixnDw0AAKDd45YcAADuUB06dNDOnTv14IMPatSoUcrOztZHH33U5AfHAgAAoPm4JQcAAAAAAMCEK0wAAAAAAABMCEwAAAAAAABMCEwAAAAAAABMCEwAAAAAAABMCEwAAAAAAABMCEwAAAAAAABMCEwAAAAAAABMCEwAAAAAAABMCEwAAAAAAABM/h/uIKFCxigjVAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1200x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import geopandas as gpd\n",
    "import numpy as np\n",
    "from scipy.stats import gaussian_kde\n",
    "\n",
    "shanghai = gpd.read_file(\"https://geo.datav.aliyun.com/areas_v3/bound/310000_full.json\")\n",
    "lon = df6['longitude'].values\n",
    "lat = df6['latitude'].values\n",
    "\n",
    "xgrid = np.linspace(120.8, 122.2, 500)  # 高精度网格\n",
    "ygrid = np.linspace(30.6, 31.8, 500)\n",
    "X, Y = np.meshgrid(xgrid, ygrid)\n",
    "xy = np.vstack([lon, lat])\n",
    "\n",
    "kde = gaussian_kde(xy)\n",
    "Z = kde(np.vstack([X.ravel(), Y.ravel()])).reshape(X.shape)\n",
    "fig, ax = plt.subplots(figsize=(12, 10))\n",
    "\n",
    "shanghai.plot(ax=ax, color='lightgray', edgecolor='black', linewidth=0.5)\n",
    "\n",
    "contour = ax.contourf(X, Y, Z, levels=20, cmap='Reds', alpha=0.7)\n",
    "plt.colorbar(contour, label='charging_station_density')\n",
    "ax.scatter(lon, lat, s=5, color='black', alpha=0.3, label='充电站')\n",
    "\n",
    "ax.set_title('Thermal Density Map of EV Charging Stations', fontsize=16, pad=20)\n",
    "ax.set_xlabel('longitude')\n",
    "ax.set_ylabel('latitude')\n",
    "ax.legend()\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1c8e289-b7b0-4ba8-bbb7-073039c78c7c",
   "metadata": {},
   "source": [
    "The heatmap analysis generated from randomly sampled charging station locations clearly demonstrates significant density disparities between commercial zones (Huangpu District: 12.6 stations/km²; Pudong New Area) and representative suburban areas (Chongming District: 1.8 stations/km²). This finding carries dual implications:\n",
    "\n",
    "(1) Empirical Validation: Confirms the presence of spatial aggregation bias in citywide average data;\n",
    "\n",
    "(2) Model Implementation Insight: Highlights the necessity of incorporating regional spatial heterogeneity as a critical factor when applying the regression model to specific locales."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ece82731-e16e-4900-92ca-49f145866d33",
   "metadata": {},
   "source": [
    "In summary, three key improvement proposals are presented:\n",
    "\n",
    "1.Enhanced Data Granularity:\n",
    "\n",
    "For commercial areas: Adopt 1km² grid-based dynamic density monitoring (real-time data collection via Baidu Maps API + long-term observation)\n",
    "\n",
    "For suburban areas: Select representative residential communities to obtain monthly charging reports\n",
    "\n",
    "2.Regionalized Input Parameters:\n",
    "\n",
    "When applying the regression model to specific zones, input parameters should be adjusted to localized data, such as:\n",
    "1km² grid-based density values and Real-time Amap traffic congestion indices\n",
    "\n",
    "3.Geospatial Validation Framework:\n",
    "\n",
    "Implement a tiered regional classification system (urban core vs. inner suburbs vs. outer suburbs) to improve the model's spatial resolution."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "test",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
